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Hello, and welcome to fire science show session 37.
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Great to have you here again.
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today's guest is professor Enrico Ronchi from Lund University.
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And you may remember him from episode 16, where I've tapped into his mind and professors.
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Ruggiero Lovreglio's mind to understand what the future has for evacuation modeling.
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I've invited Enrico after a passionate, discussion on Twitter about, protecting evacuation modeling field from bad science and how badly some models are use.
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And.
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How it creates a parallel literature scheme.
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And it's quite funny that, just today in the morning, There was a really nice, Webinar by human behavior group of IFSS Where the terminology used in evacuation science was discussed.
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And a lot of this is so relatable to my today's discussion to say, In a way, the webinar in the morning has shaped the discussion in the afternoon.
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And that makes me very happy because I think it gave the talk another dimension.
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And I hope you like how we've ventured with Enrico, through some concepts that are common in evacuation modeling and maybe they should not.
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And some concepts that are not common and maybe they should be a little more so.
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, yeah.
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whether if you are engineer or researcher, I think there's a lot of useful things to take away from this episode.
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I hope you enjoy.
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So let's not prolong this.
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And let's jump into the episode.
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Hello today I'm here with professor Enrico Ronchi from Lund university.
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Hello, Enrico.
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Great to have you.
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back in the show,
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Hi.
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Hi Wojciech.
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Very nice to be back,
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Please, don't panic.
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Uh, yes, it's, just 15 seconds into the episode and we've already used a forbidden word and It's an important part of this episode.
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Just earlier this morning.
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I, saw a great webinar, in, um, human behavior in fire work group of IFSS with,, Anne Templeton and Milad Haghani who did, discuss the P word in depth and.
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You were moderator of that.
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It was great.
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Will it be available?
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online by the way?
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we have actually YouTube channels.
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So we record all our webinars and, yeah, so I will share around the LinkedIn subscribe to our YouTube channel.
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That's so cool so two for a price of one, that you ended up listening to these episodes, go and immediately learn something about, how bad.
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we Use terminology in evacuation, modeling, but it has, consequences far beyond the, Hm.
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Cleanliness of the language, it's not just using the terminology, because the vocabulary, says so so it has a deeper meaning that leads to misconceptions, and issues with that.
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do you think it's really.
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such an important thing to have workshops on that and, really battle.
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I think in our subfield of evacuation modeling and human behavior fire, really flagged this as a very big problem because noticed that there is, uh, an even increasing, literature modeling papers.
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They try to model something that has very little link to the real world.
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And, If we look at the studies for instance, that both Anne have been doing into the use of a terminology, this seems to have spread more and more following up, these seminal papers, which make an incorrect use Of those words.
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So for instance, there is a, let's say one of the key papers in our field is the paper from, Helbing and some of his colleagues published in Nature in the middle nineties, which presents the social force model.
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Probably that's one of the most common modeling approaches in a evacuation simulations.
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it talks about modeling panic And the fingers that actually the model is a beautiful model mathematically, because it's very simple and it's very flexible in representing people movement, it is actually not modeling panic.
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And what happened is that people literally the words as they are in the, paper.
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And they start making modeling efforts, which let's say derail from theoriginal purpose of that paper, which was actually about the modeling people movement.
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And we see a lot of modeling efforts, which, , don't make too much physical sense.
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And, and it's a really a pity because, as community, we should try to intervene to do something into stopping this waste of energies.
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I will argue the property.
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This is not
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Yes.
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as we think.
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Did we have that may become from slightly different domains from ours, that approach our domain of, fire and evacuation modeling and basically, they are very good at maths.
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Probably.
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They're very good into, coding and implementing models, but they probably did not spend enough time reading the literature.
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Of the what they're meant to model, but they just look at the modeling literature and these created this very, huge bulk of modeling literature with.
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Jeez.
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Unfortunately a very poor, for the fire engineers or for any users of evacuation models, because they mostly attempt to model something that doesn't happen in real life or hyper very rarely, or that it is not the assumptions that we use in design.
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So this disconnection between the real world and the real world.
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And the modeling world unfortunately creates a lot of waste in the research community.
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So there needs to be effort to try to avoid this somehow.
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And I mean, that was the motivation for one of the motivation for our groups, on human behaviour and fires of the IFSS was indeed put ourselves together, hit some hot topics that we thought could be interesting both within the community, but also outside the community so that if someone is approaching our field, at least can have a general overview let's say, on the common mistakes the common problems that we see when a new people, approach, this field.
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Okay.
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Let's try and figure out what's really the issue is.
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So in my understanding, you can use term panic or whatever other way you would like to name it.
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But you can use this term as a proxy of a very complex human behavior.
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Like you say, okay, this group of people or this person has panicked which means there is a new rationale for their behavior.
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They will act like this.
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They will do this.
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And you also used the term That it's contagious.
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And I think that it was how we.
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Previously described it can spread from person to person.
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So Suddenly you have a fairly easy way to model behavior of large groups of people.
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Right.
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And in reality, you have evacuation scientists looking through through real cases of fires, looking through data, carrying your experiments.
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You do not see this simple negative behavior.
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I had the Erica in the podcast and she was mentioning the complexity of modeling behavior So in one way, putting this in, in this panic route or, or this simplistic behavior route gives you the benefit of modeling.
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And, you can claim that you've modeled behavior, but in world you did not.
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You just shown the proxy, which actually is incorrect.
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it's like, you're building a video game, but that's nothing to do with with reality because it's much easier.
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Let's say model social influence as a contagion, this spreads with contact with physical contact like, okay, like I touch you, you become a red dot, you wear black dot and so on, but this is not how it works.
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And that's the problem that we face.
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We face the situation in which the leadership.
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Unfortunately, it's kind of split, into a part of, of scientists that to model the real world phenomenon.
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And this, as you said, and as several other colleagues say, like Erika Kuligowski many others.
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, it is not easy.
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You can either approach this with, let's say more macroscopic approach or microscopic approach.
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Try to look at each individual person.
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But in any case, it's not simple because you will have to factor in many variables.
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One of we know is social influence, which not based in relying on who we have, around us, but also in who we are in the simulation, so that the actual profiling each individual person.
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It's also important all these models, they are already, there are a few very good models technically for modeling these things.
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But the problem is how to calibrate them real world scenarios real world data.
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Knowing that even to get hold of these data is very difficult.
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And even if we have dates.
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Of this kind, sometimes the, either come from observational studies in which you don't have level of detail let's say, what is the inner decision-making process that people do all the other side?
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The problem is when you have like a.
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Uh, instead a stated preference studies in which actually people claim what they will do, they have problems with validity because then you may have people claiming they will do something.
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But, , we do not know how often, how well this relate would actually what will happen in the real world.
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So of these type of data carry their own uncertainties and their own limitations.
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So.
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The solution is not just to make everything simple, overly simple, and just say, okay, we do a spread of contagion model and we have solved the problem.
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and we can use this for design that the solution is to try to, first of all, as scientists to understand what are these fundamental variables that affect decision-making, as modeler.
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Just let's stick to what we know from the real world.
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So go ahead with, the knowledge that we have.
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Let's not try to go one step forward into try to model things that we don't know because.
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That's not gonna work.
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That's not gonna work.
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Any model will not be meaningful for fire engineers it's not validated and if it's not properly calibrated.
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And that's what I'm saying.
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There are, there is for instance, very interesting work that will.
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A couple of years ago, also by Erica, Steve Gwynne Michael Kinsey a couple of colleagues there, Max Kinateder, they were looking at so-called behavioral statements.
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So they were saying, okay, we have a
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Yeah.
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statements we can produce, which in very short sentences, they tell us what we know about human behavior and fire.
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So what we actually can say with a certain degree of certainty that is going to happen.
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So one classic example is.
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Humans tend to satisfy rather than optimize.
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So, humans will not necessarily pick the exit that will give us the short this time, but they will pick the exit in the building that will make them arrive safely.
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They don't care if they're riding.
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10 seconds before or 10 seconds later.
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But you seen instead this kind of flawed of optimization models, which in one sense, I understand why they are there because okay.
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You want to first know what is the condition, the optimal condition in order to strive for that.
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But they see much more problems, for instance, understanding in understanding how people comply to what they're told to do rather than, knowing what is the optimal.
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So much more.
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At least my view and I'm, and I'm very opinionated about
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Um,
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much more important to put efforts into understanding what bring to comply, to information that are given either, the building itself.
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So.
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Environmental features by intervention of the, of building managers, than putting a huge effort in studying the exact optimum evacuation strategy a building, we need something that works.
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again, if it takes 10 seconds, less or 10 seconds more to an individual person will not make a difference.
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, so I think there is a.
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Wrong allocation of resources in researching these terms.
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And that probably comes from the fact that it's much easier to build an optimization model rather than doing an experiment.
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Yeah, think in the contrast to what you say about this complex modeling of behavior and even the route choice.
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In contrast to that most of the currently used models would just have some shortest path algorithm to decide which path you would take.
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Maybe some social force model to, um, push the agents from each other, maybe, maybe some model of attractiveness of exits, where you could physically alter the, how attractive one exit is from another.
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But from my experience in this practical modeling, I would say I use it mostly for engineering not for science, so I have never ventured into writing my own models or.
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I've never really went too deep beyond what my contract required.
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me to do.
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So ma You know, I'm an engineer.
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I, it's not my job to figure out the best way when the tools are on the market.
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And they are advertised are the industry standard.
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right?
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So you have this contrast of what it can be with modeling behavior and what it is mostly, which is route optimization or.
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path optimization And just movement calculations and there's nothing wrong with movement.
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calculations.
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It's just, it's not modeling of complex behavior of people.
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And Do you think this, creates this, let's say dualism in the world of evacuation science?
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I think in general terms, the key is flexibility.
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This is the key word.
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So when you use evacuation models, and I will say many commercial model developers have understood that it's much more important for a user to be able to control.
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the behavior of agents in order to be able to represent different "what if" scenarios then to have the most optimized algorithm for route choice, let's say, or for a people movement and so on.
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So I think that's something that.
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Many people in the commercial, uh, evacuation model world, have well, implemented and understood.
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The problem is actually with researchers.
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And I say this with a lot of pain in my heart, but there is a full, uh, body of literature, digs a lot into modeling things that are not really needed for engineers, they are actually starting from wrong assumptions and that's the real
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that's why
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why a lot, the efforts for instance, that a modelers do they start with the model and then they jump into.
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Data and try to validate it with data all the way around.
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data driven models in which they start with
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Yeah.
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and then they try to build them all.
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That's a very good starting point for an evacuation model.
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But don't see these.
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I mean, I see these, but I don't see as often as I would like it to see, because there is still of the literature of people that have basically never done.
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an evacuation experiments in their life and they build models or they, and even worse because that's in principle is okay that you are a specialized modeler and not into experiments, but it also, and never probably read behavior And the actual, experimental data.
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And that's the problem.
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And that's where you see this mismatch, where that's, where you see, these publications that unfortunately even get published in scientific journals are very little link, to the real ward and unfortunately, very little use, to fire engineers or in general, to any users of evacuation models, because seem like, uh, exercises done often, I don't know, a graduate students or thesis students that have to publish something.
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PA
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Um,
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to publish.
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it's mathematically beautiful.
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I don't question this.
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Maybe some people a really good at that.
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Maybe better than many of us, but it's very little use dilutes the literature in the field and he also has very little use for engineers.
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And again, I'm not arguing that all literature should be a linked to the practical ward, but you should not mislead at least engineers.
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You should not lead them into doing, design mistakes.
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And this is unfortunately what I see because this literature is so big of those kinds of papers, trying to modeling panic or contagion or, , whatever.
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Let's say behavior that has been, uh, deconstructed and, dismounted in the literature, in the field of social psychology or similar fields it is actually warring at this point because we, we are not able to stop this, , and they cite each other and, they continue doing this.
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I can make an example.
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example.
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A nice chat at some point with an applied mathematician.
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And he told me something that I always suspect is a crowd modeler.
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And the Emiliano Christiana is from Rome.
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And anytime, you know, there are some people that really fall in love, with certain mathematical equations and they will
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Yeah.
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in our field to squeeze those everywhere.
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So they say, oh, I can use this also for crowd dynamics and.
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And, for those that instead, try to link this to the real world and, and have a use of this.
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This is painful.
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is painful because you see such a waste of resources.
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So these people could be
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Yeah.
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brilliant solving, certain problems with evacuation modeling, but they putting said so much emphasis on something else that is not needed.
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So that's what we both were saying.
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Okay.
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We wished that we will have, the right effort.
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Putting the right problems, in our community.
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I think this dilution is not only, typical for evacuation science.
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I see that a lot in tunneling example, there's a group of researchers who will, determine that back layering distance or critical velocity, The, the fourth, , significant digit when, as an engineer is just double it And then make it safe.
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You don't really care that much.
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and as as you mentioned, this creates this powerful ecosystem.
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I mean, when it's just one paper that does that or one researcher who does it it can be an error It can be in mistake.
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It can be just a honest misjudgment of a scientist, but when it forms an ecosystem where this is continuously repeated, repeated, repeated, you Start building on the parallel paradigm.
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You need to build science you need to have paradigms.
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And if, in your case modeling, assuming some certain behaviors some certain extreme behaviors that do not have, Proof in really evacuation studies, a real fires.
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if that suddenly becomes a paradigm for.
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a part of your field, it, this despite the field is genuinely lost.
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And as you mentioned, that is waste of resources that is waste of human time that this waste of work sometimes, brilliant scientists who are really good at what they're doing.
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For example, applying this mathematical problems to solve something.
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And we are not a big field.
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We don't have people to lose.
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We we need all hands on deck.
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solving important problems, of our discipline.
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Do you think may be sometimes entering the spiral can be just a conscious choice because it may be somewhat easier or
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I honestly don't think so.
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I think it's generally a genuine lack of understanding of the literature, which leads to basically sticking to a certain type of terminology and a certain type of assumptions that let's say if we are split and we are, , let's say a percentage of people that claim that we should modeling panic, a contagion and so on, and let's say.
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Part of the community is trying to explain over and over and over in the last 30, 40 years.
00:20:26.204 --> 00:20:33.739
I mean, this morning we were discussing it that the first papers discussing panic misconceptions, all the come from the seventies or things like this.
00:20:33.739 --> 00:20:35.868
So it's still that we're battling.
00:20:36.463 --> 00:20:45.763
I think there is a genuinely honest problem with the community is not able to intervene to spread this misconception.
00:20:45.763 --> 00:20:59.104
And in the, in our case, this is also very much fed by media he's fed, by, you know, everything that you watch outside the scientific world because that's the problem that we have as well, maybe other domain, , modeling of fires.
00:20:59.814 --> 00:21:12.614
It is not such a big problem, in our domain, the thing is that when you turn on the TV or open a newspaper online, always this tendency to have a sensational titles and so on.
00:21:12.614 --> 00:21:15.463
So they want to use kind of extreme.
00:21:15.963 --> 00:21:25.701
wording and depict this extreme behavior, even if they didn't happen because their goal is often to sell newspapers rather than, actually, telling what, what happened.
00:21:25.701 --> 00:21:34.761
But I said, I don't think there is a need an agenda in a way it's more like a genuine mistake made by a portion of the community that is.
00:21:35.214 --> 00:21:47.578
comes another world I mentioned the world of applied mathematics, but it can be applied physics or anything that is not to the core literature or in the field of psychology, social psychology.
00:21:47.578 --> 00:21:52.182
And so on that explained us this mechanism, of human behavior and fire, work.
00:21:52.598 --> 00:21:55.868
so this is what I think is the, is the problem nowadays.
00:21:55.898 --> 00:22:16.994
And to be honest with you, I'm even more worried now with the new trends of science and machine learning and so on, because these are fantastic tools, but they, often start from another premise that we don't want to understand the fundamental mechanisms, we want to identify patterns in what happens.
00:22:17.894 --> 00:22:24.285
And often this leads to the blind trusting the data, which is the biggest problem that we have in this case.
00:22:24.674 --> 00:22:43.809
And which I think we should be very careful about because there is a huge risk that we will end up now with a whole bunch of new literature, , in the domain of evacuation using these kind of data science approaches with machine learning and so on, may really start from data that have problems.
00:22:43.809 --> 00:22:48.730
And instead they take them as good by default and come up with wrong conclusions.
00:22:48.759 --> 00:22:51.190
And as I say, don't get me wrong because.
00:22:51.775 --> 00:22:57.894
Big, a big enthusiast, myself of this kind of data science approach and machine learning.
00:22:57.894 --> 00:23:08.679
But I just have to put down a warning there for our field specifically because there is this risk, to data, and get, start getting models that.
00:23:09.141 --> 00:23:18.974
Down conclusions that do not make too much sense just because we are not putting efforts in understanding the fundamentals what is happening, but just trying to identify patterns into what happens
00:23:19.248 --> 00:23:37.476
so in my field using machine learning or artificial and any type of big data tool actually is, let's say straightforward because I'm working with fundamental physics, the conservation of energy in the mass is always the same the way how, I don't know, pressure or density changes with temperature.
00:23:37.776 --> 00:23:46.429
I can describe this with an equation, even if I have very, very complex relation between the group of variables and my outcome.
00:23:46.438 --> 00:24:01.173
It is in the physical low and here by data, I assume you mean the common tools for evacuation modelings, which would be surveys, which would be data collected from, evacuation drills and real incidents.
00:24:01.564 --> 00:24:18.309
It would be a mathematical data describing the movement of people, but mathematical data cannot describe the behavior of people or their choices, or at least not in such a straightforward way as I can describe the motion of smoke or heat transfer through a partition in the building.
00:24:18.970 --> 00:24:39.294
And so you are really like Your data by assumption has this level of There is that you first need to understand that the variability in your data set and you first need to understand what your data represents before you drop it into a black book seeking for hidden relations.
00:24:39.324 --> 00:24:40.463
Is that what you meant?
00:24:40.617 --> 00:24:42.327
I think this is exactly what I mean.
00:24:42.357 --> 00:24:45.560
And sometime I make decent example.
00:24:45.560 --> 00:25:11.123
When I talk about this, I don't know how many of you are chess players, but you've probably heard the story of alpha zero, this super software that has been built learning from millions of chess games and the game and actually having one of the main grand master number one in one ofMagnus Carlssen and learning the software that use this kind of, algorithm carying just about the final outcome, not about the chest theories on everything that we w we know from the theory.
00:25:11.722 --> 00:25:19.643
in that context, this works really well because assumptions to start with, so the movement of the pieces are what they are.
00:25:19.643 --> 00:25:23.512
So there is no, let's say a M B
00:25:23.719 --> 00:25:24.439
We showed them
00:25:24.893 --> 00:25:25.673
a straight line.
00:25:25.702 --> 00:25:26.633
They move diagonally.
00:25:26.663 --> 00:25:27.143
That's it.
00:25:27.563 --> 00:25:28.853
So instead.
00:25:28.915 --> 00:25:44.339
What we see in our field is that there are also aspects and assumptions that are really, questionable sometimes, and are they're really object of, debates how and why certain behavior, occur.
00:25:44.359 --> 00:25:52.380
and also they are depending on the local conditions that you have, because if you have a very large data set, I mean, I spoke with a very brilliant researchers.
00:25:52.819 --> 00:25:56.063
With the massive data sets of, pedestrian movement.
00:25:56.063 --> 00:26:05.123
Like for instance, I think that the group that has the largest collection of data sets on this, it's a university of Eindhoven with Alessandra Corvette and Federico Tasco.
00:26:05.153 --> 00:26:09.772
These guys, they put sensors in train station, they collect millions of trajectories.
00:26:09.772 --> 00:26:12.353
So it's really fascinating what they do.
00:26:12.962 --> 00:26:14.992
But imagine even with this kind of huge.
00:26:15.593 --> 00:26:23.903
This will still be a very specific population in a very specific context, in a very specific environment.
00:26:23.903 --> 00:26:28.613
So to generalize findings, eh, he needs another step he needs.
00:26:28.913 --> 00:26:31.403
Like I, I see those two things as complimentary.
00:26:31.432 --> 00:26:42.182
I'm really happy that these heifers are hip are happening I think they're really good, but we cannot just rely on those models try to identify patterns in these big data.
00:26:42.482 --> 00:26:43.893
We also need to try to dig up.
00:26:44.698 --> 00:26:47.337
Into understanding what are the fundamental processes?
00:26:47.904 --> 00:26:50.484
And theories that can explain we see.
00:26:50.484 --> 00:26:54.934
That's why hope that many of the people that work with these methods probably wouldn't agree with me.
00:26:55.325 --> 00:27:00.125
don't think that those data science methods should substitute what we have been doing so far.
00:27:00.125 --> 00:27:03.018
They should compliment it, but we cannot have this a subsidy.
00:27:03.434 --> 00:27:10.881
our current, theoretical methods, when it comes to those types of data, for which we have a lot of uncertainty to begin with.
00:27:10.911 --> 00:27:15.471
And as I say, we may argue for hours, what domains of fire science.
00:27:15.471 --> 00:27:18.171
They have those kinds of uncertainties, which ones they have not.
00:27:18.621 --> 00:27:23.540
heard a lot of discussion for instance, and I'm not expert in that, but I've
00:27:23.652 --> 00:27:24.731
I've heard a little discussion.
00:27:24.731 --> 00:27:25.961
on Why
00:27:26.060 --> 00:27:28.941
use deterministic models a lot infire modelling than, much
00:27:28.991 --> 00:27:29.412
watch more
00:27:29.931 --> 00:27:30.351
models.
00:27:30.381 --> 00:27:31.141
And there,
00:27:31.192 --> 00:27:31.971
there, you have a
00:27:33.330 --> 00:27:39.421
model, mostly because you have a physical phenomenon, but even in that physical phenomenon, there are so many things that can impact it.
00:27:39.421 --> 00:27:44.891
So many boundary conditions and, and all sorts of other variables that we cannot fully control.
00:27:45.380 --> 00:27:57.839
In our subfield of evacuation this is evident to me that we are not yet at the stage of science understanding of which we can completely control,, and fully understand what leads to certain behavior.
00:27:58.079 --> 00:28:03.279
So we need to be really cautious when we use, data science approaches, because they often.
00:28:04.105 --> 00:28:05.305
Take the data as they are.
00:28:05.305 --> 00:28:06.295
They don't question them.
00:28:06.295 --> 00:28:06.595
So
00:28:07.085 --> 00:28:07.654
the next day.
00:28:07.704 --> 00:28:12.625
and I think there are groups that are trying to do this in also to try to understand how
00:28:12.644 --> 00:28:12.765
Oh,
00:28:12.775 --> 00:28:24.230
treat these kinds of data in order to understand, because sensor, any device, anything can have fault or can, there can be a inner problem with the data so that's why I think we should be very cautious with this.
00:28:24.230 --> 00:28:34.881
And, and maybe I don't want to sound over negative, but I think these are very good tools, but they should be seen as complimentary, not as a substitution to everything that we've been doing so far regarding fundamental theories.
00:28:35.205 --> 00:28:36.756
I have a challenging thought for you.
00:28:37.162 --> 00:28:43.967
if I look Well, in the years of development of the tools we use to predict, evacuation processes and buildings.
00:28:44.596 --> 00:29:04.663
whether you go back to like seventies, eighties, you use graphs to solve it on a piece of paper, or you use the first spreadsheets to calculate it in computer, or you start going into computer models which were it's a glorified spreadsheets, or you go into models that model that go with, you know, movement trajectories and calculate parts, social forces, and so on.
00:29:04.933 --> 00:29:06.074
Or even you jump into.
00:29:06.523 --> 00:29:14.134
machine learning, You still have the exact same constraint related to your boundary of the human decision.
00:29:14.134 --> 00:29:14.554
process.
00:29:14.584 --> 00:29:24.116
like in every single of these tools, you are constrained by the exact same limitation of not knowing the, principles behind human behaviors.
00:29:24.116 --> 00:29:37.888
So maybe if that's the case for all from the simplest, tool to the advance or a modern tool you have, maybe that's a Place where you should push your heard of evacuation scientists to solve.
00:29:37.888 --> 00:29:39.568
maybe we need a standardized model.
00:29:39.568 --> 00:29:46.318
of a human behavior that could be just applied in research instead of, trying to figure it out, from scratch.
00:29:47.046 --> 00:29:55.086
what would be this, if you had the power to just solve it, instantly which path would you, would you go and, what would you take to deliver that to the community?
00:29:55.599 --> 00:30:03.910
I mean, there have been efforts in this direction, at least to understand some of the key variables and a key mechanism.
00:30:03.910 --> 00:30:05.049
So you wouldn't be able to fire.
00:30:05.119 --> 00:30:10.039
And as I said, I think exercise that all models.
00:30:10.559 --> 00:30:22.426
Do is to take indeed what I mentioned before this behavioral statements, are a list of statements that describe what happens with humans, during a fire emergency what do they do?
00:30:22.967 --> 00:30:26.176
And try to violate their assumptions again, against those.
00:30:26.176 --> 00:30:29.267
So to see there are assumptions, hold against this
00:30:29.353 --> 00:30:29.863
That's good.
00:30:30.196 --> 00:30:34.656
now as a researcher and also fortunately, as mentors of researchers, what they tell them.
00:30:35.372 --> 00:30:45.048
, every time that you write a research paper in which you are investigating a specific population or a specific, scenario and so on, to create these behavioral statements.
00:30:45.048 --> 00:30:48.979
So try to create simple statement that concisely.
00:30:49.598 --> 00:31:00.699
Describe, what actually happened in that given situation so that a modeler can take this statement and use it as a benchmark the assumptions that are used in the model.
00:31:00.699 --> 00:31:13.388
Because as I said, we do this exercise, if we take this knowledge that we have nowadays, and we compare it, with models, we will definitely find some models that violate through their assumptions, some of these statements.
00:31:14.618 --> 00:31:23.108
as I say, the trends that I see, especially in the commercial world, and that's a, a bit strange because I would have expected more researchers to keep up with.
00:31:23.858 --> 00:31:25.608
Actually see it much more in the commercial world.
00:31:26.419 --> 00:31:38.205
model developers in the evacuation world are trying to actually give flexibility to their model so that they, at least you can make sure that you can, calibrate the model in a way that doesn't violate certain assumptions, that you have.
00:31:38.236 --> 00:31:48.301
But as I said, in, in the research world unfortunately, it happens often that want to reinvent the wheel from scratch and that's not probably the right approach.
00:31:48.332 --> 00:32:00.625
I mean, if I will never imagistic and decide what to do with the research funding in the vacation world, in the world, I would basically put 90% of my money on data collection and theory and
00:32:00.671 --> 00:32:00.924
The,
00:32:00.924 --> 00:32:02.625
the modeling be done by the
00:32:03.000 --> 00:32:03.151
um,
00:32:03.414 --> 00:32:05.355
model developers in companies.
00:32:05.355 --> 00:32:08.744
They probably are much better writing codes than all of us.
00:32:09.055 --> 00:32:11.515
is at least how I will, how we'll do it,
00:32:11.565 --> 00:32:17.625
so these rules could be, Or can be applied as some sort of sanity check if you're modeling makes sense.
00:32:17.625 --> 00:32:17.894
Right?
00:32:18.445 --> 00:32:19.405
Yeah, that's the idea.
00:32:19.405 --> 00:32:34.231
So I mean to, try to benchmark the assumptions that you use against athose simple rules and, you know, there have been simple rules defined in different domains of the evacuation world that have been these, be able to statements that come from our own community for fire safety science.
00:32:34.821 --> 00:32:44.796
for instance, there are a lot of, efforts done in the pedestrian, evacuation dynamics community, instance, in identifying, rules of, interactions between people in a crowd.
00:32:45.155 --> 00:32:50.945
So at a more microscopic level, see, how does a lane of people get formed?
00:32:51.500 --> 00:32:56.510
Brilliant researching this or how actually queuing happens.
00:32:56.550 --> 00:33:07.090
So, trying to extract those rules from the individual papers, boil them down in simple statements and then benchmark the models that we have out there against those.
00:33:07.510 --> 00:33:08.980
see, does this get violated?
00:33:09.520 --> 00:33:13.121
To begin with, is this inbuilt in the defaults of the models?
00:33:13.721 --> 00:33:19.181
we modify the default so that at least we can tune the model in a way that these assumptions are not violated any more?
00:33:19.181 --> 00:33:24.851
So this is the kind of benchmarking work that I would like to see more and more.
00:33:24.851 --> 00:33:30.590
And I mean, it's all linked to the efforts that have been done over the years in and validating, evacuation models.
00:33:30.590 --> 00:33:31.131
You know, this
00:33:31.186 --> 00:33:31.457
Y,
00:33:31.490 --> 00:33:31.911
of my.
00:33:32.011 --> 00:33:38.400
main, life mission as a researcher to develop standardized testing for over evacuation models.
00:33:38.849 --> 00:33:39.450
But as I said,
00:33:39.480 --> 00:33:50.279
as I said, we need to have another step the community trying to make their simple enough to be directly implemented into models.
00:33:50.509 --> 00:33:52.640
I have one more challenging.
00:33:52.640 --> 00:33:53.750
One asset.
00:33:53.779 --> 00:34:30.114
so I think in most of these approaches, we focus very much on the behavior of a single agent or a single person, that evacuate, but Do we truly need this microscopic approach to simulate the population, you know, to see large crowd patterns to see queuing, is, this the only way, because we started this with the issues with papers and his part of the science that goes in a slightly wrong way, because they misunderstand some of the assumptions and, to do things that are putting a lot of effort in researching things.
00:34:30.114 --> 00:34:31.344
that makes no make no sense.
00:34:31.463 --> 00:34:32.244
basically.
00:34:32.664 --> 00:34:43.789
so maybe if we could break the product that you modeled an agent, maybe there are waste, to skip that individual behavior and just focus on like group behavior or something.
00:34:43.789 --> 00:34:44.239
bigger.
00:34:44.719 --> 00:34:47.059
And how do you do wild land interface, fire?
00:34:47.059 --> 00:34:49.550
Do you, do still stick to agent or.
00:34:49.925 --> 00:34:53.543
I think this is a, another very good point of discussion.
00:34:53.543 --> 00:35:06.353
I had several discussions about this with different researchers that work in different domain, both in the WUI fire world and in the crowd, evacuation world, I think there is not a type of modeling approach that works for everything.
00:35:06.523 --> 00:35:07.643
I think it really depends.
00:35:08.527 --> 00:35:22.284
Problem that you need to solve because some cases it is true to, you could actually use a very simple, say flow calculation or a very simple microscopic model and get a fairly decent approximation of the result that you need.
00:35:22.827 --> 00:35:34.001
in some other contexts is actually probably not enough, especially when you, the impact of an individual, a behavior or a small group of individuals can, uh, can be huge on your total evacuation time.
00:35:34.391 --> 00:35:35.201
Then you can adjust.
00:35:35.686 --> 00:35:42.425
The big picture you may get these diluted and maybe let's say overlook certain aspects.
00:35:42.425 --> 00:35:50.706
So discussion came up a lot, for instance, when it comes to WUI fires because there, there is even a lot of people arguing that we don't even have to
00:35:50.942 --> 00:35:51.211
Yes.
00:35:51.485 --> 00:35:57.496
pedestrian execution modeling within with our modeling because of the scale of the traffic.
00:35:57.496 --> 00:36:00.585
When you have scenarios, we, traffic evacuation is much bigger.
00:36:00.925 --> 00:36:04.992
but again, I will not the answer like yes we need or no, we don't.
00:36:05.353 --> 00:36:10.902
The answer is, it really depends on which scenario you're looking at and what conditions you're looking at.
00:36:10.902 --> 00:36:12.552
Both for buildings and WUI fires.
00:36:12.936 --> 00:36:16.385
we should be able to have in the available.
00:36:16.385 --> 00:36:23.967
a pool of tools then select the most appropriate one, given problems that we need to solve in the design issues that we have.
00:36:23.967 --> 00:36:29.518
So will not argue, I will not recommend to just abandon a microscopic modeling.
00:36:29.547 --> 00:36:31.197
That's definitely not a good idea.
00:36:31.228 --> 00:36:36.068
Uh, personally, I think, nor abandoned macroscopic modeling, because that is also useful
00:36:36.079 --> 00:36:36.168
Okay.
00:36:36.217 --> 00:36:38.858
many instances, but more like choosing.
00:36:39.413 --> 00:36:45.882
What is the right path that we need to take, depending on what applications we have and have those two tracks developing in parallel.
00:36:45.882 --> 00:36:58.867
So maybe microscopic models can learn something from microscopic models and vice versa, , because you know, there've been also efforts trying to combine those and the using,, hybrid, approaches and so on, but that's not a trivial,
00:36:59.389 --> 00:37:02.980
would have to decide when a person changes from, , a person into a population.
00:37:03.150 --> 00:37:13.945
It's this, this sharp edge when If you would agree that that's some point that the population scale, these individual decisions have less and less impact because essentially they become statistics.
00:37:14.432 --> 00:37:16.889
if you care about the movement of a 1 million people.
00:37:17.369 --> 00:37:20.869
the decision of of a single person ends up being a statistics that's it.
00:37:21.199 --> 00:37:22.400
So, finding , this.
00:37:22.436 --> 00:37:25.485
Edge between them and applying correct approaches.
00:37:25.485 --> 00:37:49.516
to each, maybe an important, aspect of modeling overall And, I think in the same way, it's when you go from modeling agent movement into agent behavior, it's also a, a, shop interface where you literally stopped doing something very simple and jumped into a field where more complicated things, dictate the outcome.
00:37:49.757 --> 00:38:05.708
and like, honestly, from your experience, do you think in I don't know, next five 10 years we will have reliable behavioral models to use in our modeling, like I go today, I start up computer software of my choice to model.
00:38:05.708 --> 00:38:10.655
my, building, I pop in the pre evacuation distribution times and, I run the simulations.
00:38:10.887 --> 00:38:19.315
Will I be able in like five to 10 years, be able to just put default human behavior, model and then just run the same simulations without this.
00:38:20.083 --> 00:38:29.465
I think in five years, no, probably I don't want to be over optimistic because that is something that we have been looking around already for quite some time.
00:38:29.496 --> 00:38:41.856
And, and we are digging into this, but I will argue the tools that we have today and as mentioned several of them, they are becoming more and more flexible in a way that have as users, as solid understanding.
00:38:42.521 --> 00:38:47.170
Of what are the scenarios and the behaviors that we want to represent.
00:38:47.740 --> 00:38:51.221
can, we actually have the design tools to do our design nowadays.
00:38:51.221 --> 00:39:01.621
So is missing here is a more research towards of everything that has to do with those outliers scenarios that we have.
00:39:02.786 --> 00:39:14.936
Understanding and control on because we know that in certain situations they may be a small group or of people doing a certain behavior that might dramatically affect our results.
00:39:14.965 --> 00:39:20.353
And, you know, when we design a performance based deisgn fire engineering, we actually look at RSET.
00:39:20.373 --> 00:39:25.036
So we look at the actual, , tail of, our evacuation time curves.
00:39:25.255 --> 00:39:27.005
And that's where I would like to.
00:39:27.367 --> 00:39:32.847
more research too, because now we can do it, but we have a, quite a large uncertainty in this.
00:39:32.847 --> 00:39:41.050
So I hope, and I really think that we will doing 10 years from now is to basically work more on, making a small.
00:39:41.429 --> 00:39:45.940
gap what is the result that we expect and the uncertainty that we
00:39:46.164 --> 00:39:46.885
Amazingly.
00:39:47.079 --> 00:39:55.960
to basically, uh, have anRSET because, you know, when we work with RSET ASET, sometimes actually we recommend to have these probabilistic approaches to.
00:39:56.474 --> 00:40:00.945
To make sure that in 10 years from now, this uncertainty is reduced.
00:40:00.974 --> 00:40:08.664
So span of possible results become smaller, which means that we have more confidence in our, RSET when we do our design.
00:40:08.675 --> 00:40:11.695
So this is what I see happening 10 years from now.
00:40:11.724 --> 00:40:18.213
I don't think we're going to be able to pin down to one RSET for each very specific scenario because also.
00:40:18.213 --> 00:40:22.735
That's not really how we work and how are.
00:40:22.764 --> 00:40:39.934
It's very, there's so many variables that can play a role that it's very hard to isolate them all, especially in the timeframe of five or 10 years from now, but we can improve our understanding of this uncertainty and decrease that uncertainty that we actually have a better understanding of what these rset.
00:40:39.954 --> 00:40:42.054
This requires safety egress time is.
00:40:42.210 --> 00:40:52.664
I just can't stop myself from asking you for predictions for the future and for the new listeners of the podcast, there's like a whole episode with two Italians rambling on how the future of evacuation.
00:40:52.664 --> 00:40:53.594
modeling will look like.
00:40:53.594 --> 00:41:02.324
And I I highly recommend that, , the out it was a great discussion and I would also love to recommend your episode with Erica who.
00:41:02.949 --> 00:41:07.056
Explains how she's modeling behavior in wild land fires.
00:41:07.056 --> 00:41:09.695
And that is, that is truly beautiful.
00:41:09.706 --> 00:41:15.635
on the, how this aspects of human behavior, are being modeled and what it takes to actually model them.
00:41:15.876 --> 00:41:19.177
Maybe not the buildings outside of buildings, but still it's, Similar problem.
00:41:19.177 --> 00:41:24.088
And I absolutely love how she presented it and it really, shows the complexity of the problem.
00:41:24.168 --> 00:41:29.875
And I think through a resources like this, true research like that we, truly bring.
00:41:29.905 --> 00:41:33.144
more people closer to understanding.
00:41:33.880 --> 00:41:36.130
What is the big problem in here?
00:41:36.130 --> 00:41:37.960
what is the big picture in here?
00:41:38.409 --> 00:41:52.465
And if to understand that they have to move back to fundamentals and by moving back, to fundamentals, they move away from doing this misjudge conceptions that, you can simplify a human behavior into, , a phenomenon of panic Or whatever else.
00:41:52.465 --> 00:41:55.076
And, uh, I guess th that could be like a good.
00:41:55.076 --> 00:41:59.333
Summary on where should we go educate people on the complexity?
00:41:59.333 --> 00:42:08.637
And what it really means, to, to model behavior, to stop this overflow of bad science that in the end influences our discipline.
00:42:09.152 --> 00:42:09.534
Correct.
00:42:09.563 --> 00:42:11.784
And I think this is a good take home message.
00:42:11.784 --> 00:42:22.456
So, , if you are, a user of models even if you are a researcher, back and start questioning, the assumptions, both that the model use.
00:42:23.047 --> 00:42:39.873
And that you are using because this is one of the key aspects that can lead, to mistakes and problems in your simulation world So I think, these should be the take home message, when it comes to, representation of such a complex problem, like human behavior and fire.
00:42:39.994 --> 00:42:40.498
Fantastic.
00:42:40.498 --> 00:42:41.900
that's a great, summary.
00:42:41.900 --> 00:42:52.978
I I link all the resources into show notes, and I hope we've helped some scientists to get on the correct path and others to, to help us.
00:42:53.059 --> 00:43:03.452
, fight or battle of, bad science, thank you very much Enrico for coming to the show for the second time, it was a huge pleasure to have you here.
00:43:03.452 --> 00:43:08.222
And, uh, I have a feeling we will see each other, not so far from now.
00:43:08.222 --> 00:43:10.893
Again, maybe touching on the RSET concert.
00:43:10.922 --> 00:43:11.193
Oh man.
00:43:11.193 --> 00:43:13.233
That would be, that would be fun.
00:43:13.628 --> 00:43:13.996
thanks.
00:43:13.996 --> 00:43:20.547
Wojciech was very nice to be back and, thanks a lot for all the efforts and time you put into this podcast because it's really fantastic.
00:43:20.567 --> 00:43:21.646
We really needed it.
00:43:21.829 --> 00:43:22.400
Oh, thank you.
00:43:22.650 --> 00:43:23.420
I'm so happy.
00:43:23.420 --> 00:43:24.739
I didn't know what, I didn't know.
00:43:24.739 --> 00:43:25.309
we needed it.
00:43:25.340 --> 00:43:27.469
until I started it and now I just can't stop.
00:43:27.889 --> 00:43:29.179
It's really cool experience.
00:43:29.210 --> 00:43:29.730
Thank you.
00:43:30.626 --> 00:43:30.987
Thanks.
00:43:30.987 --> 00:43:31.485
Bye-bye.
00:43:32.166 --> 00:43:34.746
I hope you've enjoyed this episode.
00:43:35.317 --> 00:43:45.067
And if there's one takeaway that you can take from it, I guess a sanity check approach for testing your evacuation model would be the thing to take.
00:43:45.577 --> 00:43:50.376
Enrico mentioned the paper and I found it, the paper is
00:43:50.376 --> 00:43:57.795
Guidance for the model developer on representing human behavior in egress models and it was published in Fire and Technology journal.
00:43:58.505 --> 00:44:02.255
It's authored by a Gwynne, Hulse and Kinsey.
00:44:02.735 --> 00:44:04.025
And I link.
00:44:04.445 --> 00:44:06.726
It in the episode description, obviously.
00:44:07.436 --> 00:44:10.715
Enrico mentioned the paper contains statements.
00:44:10.746 --> 00:44:11.976
And in fact it does.
00:44:12.260 --> 00:44:18.356
Statements that, are, let's say the things that we know about the behavior of people when they evacuate.
00:44:18.925 --> 00:44:24.626
And this can be used to cross check your analysis, whether you break this statements or not.
00:44:24.760 --> 00:44:31.420
And if you do well, then most likely there is some error or misconception or the model is used in an incorrect way.
00:44:31.960 --> 00:44:32.380
And.
00:44:32.380 --> 00:44:36.090
As we don't have yet the perfect models as we don't.
00:44:36.090 --> 00:44:39.498
Have you had the ability to just easily model the.
00:44:39.748 --> 00:44:41.458
Evacuation behavior of people.
00:44:41.481 --> 00:44:47.485
Actually cross-checking our simulations with such a sanity check is, sounds like a pretty powerful.
00:44:47.490 --> 00:44:49.771
Tool that can be used to make sure that.
00:44:49.791 --> 00:44:58.657
And modeling is of high quality, and I would recommend it to engineers and I would highly recommend it to scientists who research in this field.
00:44:59.137 --> 00:45:03.847
And as mentioned in the start of the episode, I would also highly recommend listening to that.
00:45:04.427 --> 00:45:06.057
IAFSS webinar.
00:45:06.228 --> 00:45:10.717
There was a channel on YouTube, uh, with, these webinars did this one was the third one.
00:45:11.257 --> 00:45:12.128
And they're all great.
00:45:12.128 --> 00:45:13.833
So I hope this is another.
00:45:13.833 --> 00:45:16.099
resource that is interesting to you.
00:45:16.114 --> 00:45:18.367
And, I hope you'll reach out for, for that.
00:45:18.998 --> 00:45:20.918
Thank you for being here with me.
00:45:21.338 --> 00:45:24.188
Thank you for another week of podcasting.
00:45:24.697 --> 00:45:27.697
I look forward to the next week.
00:45:27.728 --> 00:45:29.815
So see you again next Wednesday.
00:45:29.844 --> 00:45:30.175
Thank you.
00:45:30.175 --> 00:45:30.565
Bye.
00:00:01.229 --> 00:00:04.290
Hello, and welcome to fire science show session 37.
00:00:04.290 --> 00:00:05.459
Great to have you here again.
00:00:05.937 --> 00:00:10.256
today's guest is professor Enrico Ronchi from Lund University.
00:00:10.285 --> 00:00:16.449
And you may remember him from episode 16, where I've tapped into his mind and professors.
00:00:16.449 --> 00:00:22.030
Ruggiero Lovreglio's mind to understand what the future has for evacuation modeling.
00:00:22.599 --> 00:00:33.820
I've invited Enrico after a passionate, discussion on Twitter about, protecting evacuation modeling field from bad science and how badly some models are use.
00:00:33.850 --> 00:00:34.000
And.
00:00:34.840 --> 00:00:38.210
How it creates a parallel literature scheme.
00:00:38.689 --> 00:00:53.152
And it's quite funny that, just today in the morning, There was a really nice, Webinar by human behavior group of IFSS Where the terminology used in evacuation science was discussed.
00:00:53.872 --> 00:01:02.393
And a lot of this is so relatable to my today's discussion to say, In a way, the webinar in the morning has shaped the discussion in the afternoon.
00:01:02.753 --> 00:01:08.033
And that makes me very happy because I think it gave the talk another dimension.
00:01:08.316 --> 00:01:16.632
And I hope you like how we've ventured with Enrico, through some concepts that are common in evacuation modeling and maybe they should not.
00:01:16.753 --> 00:01:22.088
And some concepts that are not common and maybe they should be a little more so.
00:01:22.099 --> 00:01:22.429
, yeah.
00:01:22.475 --> 00:01:29.638
whether if you are engineer or researcher, I think there's a lot of useful things to take away from this episode.
00:01:30.058 --> 00:01:31.168
I hope you enjoy.
00:01:31.379 --> 00:01:33.418
So let's not prolong this.
00:01:33.439 --> 00:01:35.179
And let's jump into the episode.
00:01:58.251 --> 00:02:02.510
Hello today I'm here with professor Enrico Ronchi from Lund university.
00:02:02.540 --> 00:02:03.171
Hello, Enrico.
00:02:03.171 --> 00:02:03.861
Great to have you.
00:02:03.891 --> 00:02:04.691
back in the show,
00:02:04.980 --> 00:02:05.441
Hi.
00:02:05.471 --> 00:02:06.150
Hi Wojciech.
00:02:06.150 --> 00:02:07.251
Very nice to be back,
00:02:07.691 --> 00:02:08.647
Please, don't panic.
00:02:12.578 --> 00:02:20.229
Uh, yes, it's, just 15 seconds into the episode and we've already used a forbidden word and It's an important part of this episode.
00:02:20.328 --> 00:02:21.978
Just earlier this morning.
00:02:22.000 --> 00:02:34.075
I, saw a great webinar, in, um, human behavior in fire work group of IFSS with,, Anne Templeton and Milad Haghani who did, discuss the P word in depth and.
00:02:34.395 --> 00:02:36.045
You were moderator of that.
00:02:36.049 --> 00:02:36.709
It was great.
00:02:36.919 --> 00:02:38.088
Will it be available?
00:02:38.088 --> 00:02:39.019
online by the way?
00:02:39.299 --> 00:02:40.919
we have actually YouTube channels.
00:02:40.919 --> 00:02:48.562
So we record all our webinars and, yeah, so I will share around the LinkedIn subscribe to our YouTube channel.
00:02:49.162 --> 00:02:57.361
That's so cool so two for a price of one, that you ended up listening to these episodes, go and immediately learn something about, how bad.
00:02:57.451 --> 00:03:04.731
we Use terminology in evacuation, modeling, but it has, consequences far beyond the, Hm.
00:03:05.461 --> 00:03:17.225
Cleanliness of the language, it's not just using the terminology, because the vocabulary, says so so it has a deeper meaning that leads to misconceptions, and issues with that.
00:03:17.841 --> 00:03:19.111
do you think it's really.
00:03:19.382 --> 00:03:24.276
such an important thing to have workshops on that and, really battle.
00:03:24.842 --> 00:03:41.378
I think in our subfield of evacuation modeling and human behavior fire, really flagged this as a very big problem because noticed that there is, uh, an even increasing, literature modeling papers.
00:03:41.679 --> 00:03:46.209
They try to model something that has very little link to the real world.
00:03:46.239 --> 00:04:03.144
And, If we look at the studies for instance, that both Anne have been doing into the use of a terminology, this seems to have spread more and more following up, these seminal papers, which make an incorrect use Of those words.
00:04:03.144 --> 00:04:14.001
So for instance, there is a, let's say one of the key papers in our field is the paper from, Helbing and some of his colleagues published in Nature in the middle nineties, which presents the social force model.
00:04:14.031 --> 00:04:18.951
Probably that's one of the most common modeling approaches in a evacuation simulations.
00:04:19.490 --> 00:04:32.163
it talks about modeling panic And the fingers that actually the model is a beautiful model mathematically, because it's very simple and it's very flexible in representing people movement, it is actually not modeling panic.
00:04:32.793 --> 00:04:39.562
And what happened is that people literally the words as they are in the, paper.
00:04:39.593 --> 00:04:48.798
And they start making modeling efforts, which let's say derail from theoriginal purpose of that paper, which was actually about the modeling people movement.
00:04:49.519 --> 00:04:54.738
And we see a lot of modeling efforts, which, , don't make too much physical sense.
00:04:54.769 --> 00:05:02.055
And, and it's a really a pity because, as community, we should try to intervene to do something into stopping this waste of energies.
00:05:02.504 --> 00:05:03.786
I will argue the property.
00:05:03.805 --> 00:05:04.956
This is not
00:05:05.581 --> 00:05:05.880
Yes.
00:05:06.305 --> 00:05:07.055
as we think.
00:05:07.415 --> 00:05:19.492
Did we have that may become from slightly different domains from ours, that approach our domain of, fire and evacuation modeling and basically, they are very good at maths.
00:05:19.492 --> 00:05:20.122
Probably.
00:05:20.122 --> 00:05:27.723
They're very good into, coding and implementing models, but they probably did not spend enough time reading the literature.
00:05:28.338 --> 00:05:39.480
Of the what they're meant to model, but they just look at the modeling literature and these created this very, huge bulk of modeling literature with.
00:05:39.781 --> 00:05:40.093
Jeez.
00:05:40.119 --> 00:05:55.879
Unfortunately a very poor, for the fire engineers or for any users of evacuation models, because they mostly attempt to model something that doesn't happen in real life or hyper very rarely, or that it is not the assumptions that we use in design.
00:05:55.879 --> 00:06:00.798
So this disconnection between the real world and the real world.
00:06:01.494 --> 00:06:07.584
And the modeling world unfortunately creates a lot of waste in the research community.
00:06:07.584 --> 00:06:11.963
So there needs to be effort to try to avoid this somehow.
00:06:11.994 --> 00:06:39.682
And I mean, that was the motivation for one of the motivation for our groups, on human behaviour and fires of the IFSS was indeed put ourselves together, hit some hot topics that we thought could be interesting both within the community, but also outside the community so that if someone is approaching our field, at least can have a general overview let's say, on the common mistakes the common problems that we see when a new people, approach, this field.
00:06:39.870 --> 00:06:40.259
Okay.
00:06:40.415 --> 00:06:43.615
Let's try and figure out what's really the issue is.
00:06:44.185 --> 00:06:51.961
So in my understanding, you can use term panic or whatever other way you would like to name it.
00:06:52.442 --> 00:06:56.528
But you can use this term as a proxy of a very complex human behavior.
00:06:56.649 --> 00:07:05.079
Like you say, okay, this group of people or this person has panicked which means there is a new rationale for their behavior.
00:07:05.439 --> 00:07:06.968
They will act like this.
00:07:06.968 --> 00:07:08.199
They will do this.
00:07:08.199 --> 00:07:10.656
And you also used the term That it's contagious.
00:07:10.656 --> 00:07:11.935
And I think that it was how we.
00:07:12.430 --> 00:07:15.190
Previously described it can spread from person to person.
00:07:15.670 --> 00:07:21.500
So Suddenly you have a fairly easy way to model behavior of large groups of people.
00:07:21.500 --> 00:07:21.891
Right.
00:07:21.920 --> 00:07:31.766
And in reality, you have evacuation scientists looking through through real cases of fires, looking through data, carrying your experiments.
00:07:32.096 --> 00:07:35.228
You do not see this simple negative behavior.
00:07:35.598 --> 00:07:49.805
I had the Erica in the podcast and she was mentioning the complexity of modeling behavior So in one way, putting this in, in this panic route or, or this simplistic behavior route gives you the benefit of modeling.
00:07:50.543 --> 00:07:54.113
And, you can claim that you've modeled behavior, but in world you did not.
00:07:54.113 --> 00:07:56.903
You just shown the proxy, which actually is incorrect.
00:07:57.264 --> 00:08:02.576
it's like, you're building a video game, but that's nothing to do with with reality because it's much easier.
00:08:02.971 --> 00:08:17.379
Let's say model social influence as a contagion, this spreads with contact with physical contact like, okay, like I touch you, you become a red dot, you wear black dot and so on, but this is not how it works.
00:08:17.978 --> 00:08:20.019
And that's the problem that we face.
00:08:20.019 --> 00:08:23.048
We face the situation in which the leadership.
00:08:23.569 --> 00:08:31.149
Unfortunately, it's kind of split, into a part of, of scientists that to model the real world phenomenon.
00:08:31.178 --> 00:08:35.948
And this, as you said, and as several other colleagues say, like Erika Kuligowski many others.
00:08:36.349 --> 00:08:37.788
, it is not easy.
00:08:38.511 --> 00:08:44.211
You can either approach this with, let's say more macroscopic approach or microscopic approach.
00:08:44.241 --> 00:08:46.942
Try to look at each individual person.
00:08:46.942 --> 00:08:50.631
But in any case, it's not simple because you will have to factor in many variables.
00:08:50.662 --> 00:09:03.950
One of we know is social influence, which not based in relying on who we have, around us, but also in who we are in the simulation, so that the actual profiling each individual person.
00:09:04.695 --> 00:09:12.065
It's also important all these models, they are already, there are a few very good models technically for modeling these things.
00:09:12.065 --> 00:09:18.394
But the problem is how to calibrate them real world scenarios real world data.
00:09:18.424 --> 00:09:21.754
Knowing that even to get hold of these data is very difficult.
00:09:22.085 --> 00:09:23.134
And even if we have dates.
00:09:23.955 --> 00:09:37.328
Of this kind, sometimes the, either come from observational studies in which you don't have level of detail let's say, what is the inner decision-making process that people do all the other side?
00:09:37.359 --> 00:09:39.068
The problem is when you have like a.
00:09:39.573 --> 00:09:49.994
Uh, instead a stated preference studies in which actually people claim what they will do, they have problems with validity because then you may have people claiming they will do something.
00:09:50.024 --> 00:09:55.683
But, , we do not know how often, how well this relate would actually what will happen in the real world.
00:09:55.683 --> 00:10:00.864
So of these type of data carry their own uncertainties and their own limitations.
00:10:00.864 --> 00:10:01.024
So.
00:10:01.524 --> 00:10:11.964
The solution is not just to make everything simple, overly simple, and just say, okay, we do a spread of contagion model and we have solved the problem.
00:10:12.403 --> 00:10:22.144
and we can use this for design that the solution is to try to, first of all, as scientists to understand what are these fundamental variables that affect decision-making, as modeler.
00:10:22.854 --> 00:10:25.974
Just let's stick to what we know from the real world.
00:10:25.974 --> 00:10:28.940
So go ahead with, the knowledge that we have.
00:10:28.940 --> 00:10:34.850
Let's not try to go one step forward into try to model things that we don't know because.
00:10:35.565 --> 00:10:36.884
That's not gonna work.
00:10:36.914 --> 00:10:37.845
That's not gonna work.
00:10:37.875 --> 00:10:45.554
Any model will not be meaningful for fire engineers it's not validated and if it's not properly calibrated.
00:10:45.554 --> 00:10:47.235
And that's what I'm saying.
00:10:47.294 --> 00:10:49.695
There are, there is for instance, very interesting work that will.
00:10:50.451 --> 00:10:58.499
A couple of years ago, also by Erica, Steve Gwynne Michael Kinsey a couple of colleagues there, Max Kinateder, they were looking at so-called behavioral statements.
00:10:58.589 --> 00:11:01.019
So they were saying, okay, we have a
00:11:01.058 --> 00:11:01.418
Yeah.
00:11:01.499 --> 00:11:09.328
statements we can produce, which in very short sentences, they tell us what we know about human behavior and fire.
00:11:09.328 --> 00:11:14.249
So what we actually can say with a certain degree of certainty that is going to happen.
00:11:14.249 --> 00:11:16.318
So one classic example is.
00:11:16.808 --> 00:11:20.318
Humans tend to satisfy rather than optimize.
00:11:20.349 --> 00:11:30.024
So, humans will not necessarily pick the exit that will give us the short this time, but they will pick the exit in the building that will make them arrive safely.
00:11:30.024 --> 00:11:31.075
They don't care if they're riding.
00:11:31.565 --> 00:11:34.384
10 seconds before or 10 seconds later.
00:11:34.774 --> 00:11:42.304
But you seen instead this kind of flawed of optimization models, which in one sense, I understand why they are there because okay.
00:11:42.304 --> 00:11:46.955
You want to first know what is the condition, the optimal condition in order to strive for that.
00:11:47.375 --> 00:11:57.235
But they see much more problems, for instance, understanding in understanding how people comply to what they're told to do rather than, knowing what is the optimal.
00:11:57.475 --> 00:11:58.674
So much more.
00:11:59.629 --> 00:12:02.570
At least my view and I'm, and I'm very opinionated about
00:12:02.640 --> 00:12:02.821
Um,
00:12:03.049 --> 00:12:12.279
much more important to put efforts into understanding what bring to comply, to information that are given either, the building itself.
00:12:12.309 --> 00:12:12.580
So.
00:12:13.025 --> 00:12:25.769
Environmental features by intervention of the, of building managers, than putting a huge effort in studying the exact optimum evacuation strategy a building, we need something that works.
00:12:26.024 --> 00:12:32.414
again, if it takes 10 seconds, less or 10 seconds more to an individual person will not make a difference.
00:12:32.495 --> 00:12:33.634
, so I think there is a.
00:12:34.225 --> 00:12:37.764
Wrong allocation of resources in researching these terms.
00:12:37.825 --> 00:12:43.284
And that probably comes from the fact that it's much easier to build an optimization model rather than doing an experiment.
00:12:43.691 --> 00:12:51.557
Yeah, think in the contrast to what you say about this complex modeling of behavior and even the route choice.
00:12:52.403 --> 00:13:01.732
In contrast to that most of the currently used models would just have some shortest path algorithm to decide which path you would take.
00:13:01.972 --> 00:13:17.442
Maybe some social force model to, um, push the agents from each other, maybe, maybe some model of attractiveness of exits, where you could physically alter the, how attractive one exit is from another.
00:13:17.832 --> 00:13:28.342
But from my experience in this practical modeling, I would say I use it mostly for engineering not for science, so I have never ventured into writing my own models or.
00:13:28.852 --> 00:13:33.052
I've never really went too deep beyond what my contract required.
00:13:33.052 --> 00:13:33.562
me to do.
00:13:33.873 --> 00:13:35.763
So ma You know, I'm an engineer.
00:13:35.793 --> 00:13:40.508
I, it's not my job to figure out the best way when the tools are on the market.
00:13:40.508 --> 00:13:43.177
And they are advertised are the industry standard.
00:13:43.177 --> 00:13:43.447
right?
00:13:44.138 --> 00:13:53.836
So you have this contrast of what it can be with modeling behavior and what it is mostly, which is route optimization or.
00:13:53.836 --> 00:13:59.115
path optimization And just movement calculations and there's nothing wrong with movement.
00:13:59.115 --> 00:13:59.956
calculations.
00:14:00.405 --> 00:14:03.495
It's just, it's not modeling of complex behavior of people.
00:14:04.096 --> 00:14:10.793
And Do you think this, creates this, let's say dualism in the world of evacuation science?
00:14:11.072 --> 00:14:15.940
I think in general terms, the key is flexibility.
00:14:15.940 --> 00:14:16.929
This is the key word.
00:14:17.350 --> 00:14:25.659
So when you use evacuation models, and I will say many commercial model developers have understood that it's much more important for a user to be able to control.
00:14:26.154 --> 00:14:38.965
the behavior of agents in order to be able to represent different "what if" scenarios then to have the most optimized algorithm for route choice, let's say, or for a people movement and so on.
00:14:38.965 --> 00:14:40.575
So I think that's something that.
00:14:41.129 --> 00:14:46.566
Many people in the commercial, uh, evacuation model world, have well, implemented and understood.
00:14:46.926 --> 00:14:49.566
The problem is actually with researchers.
00:14:49.566 --> 00:15:10.755
And I say this with a lot of pain in my heart, but there is a full, uh, body of literature, digs a lot into modeling things that are not really needed for engineers, they are actually starting from wrong assumptions and that's the real
00:15:11.072 --> 00:15:11.402
that's why
00:15:11.416 --> 00:15:18.946
why a lot, the efforts for instance, that a modelers do they start with the model and then they jump into.
00:15:19.451 --> 00:15:22.181
Data and try to validate it with data all the way around.
00:15:22.721 --> 00:15:24.461
data driven models in which they start with
00:15:24.476 --> 00:15:24.626
Yeah.
00:15:24.941 --> 00:15:26.321
and then they try to build them all.
00:15:26.350 --> 00:15:31.177
That's a very good starting point for an evacuation model.
00:15:31.177 --> 00:15:32.798
But don't see these.
00:15:32.827 --> 00:15:41.077
I mean, I see these, but I don't see as often as I would like it to see, because there is still of the literature of people that have basically never done.
00:15:41.128 --> 00:15:59.154
an evacuation experiments in their life and they build models or they, and even worse because that's in principle is okay that you are a specialized modeler and not into experiments, but it also, and never probably read behavior And the actual, experimental data.
00:15:59.154 --> 00:16:00.174
And that's the problem.
00:16:00.174 --> 00:16:27.779
And that's where you see this mismatch, where that's, where you see, these publications that unfortunately even get published in scientific journals are very little link, to the real ward and unfortunately, very little use, to fire engineers or in general, to any users of evacuation models, because seem like, uh, exercises done often, I don't know, a graduate students or thesis students that have to publish something.
00:16:28.309 --> 00:16:28.580
PA
00:16:28.796 --> 00:16:28.916
Um,
00:16:28.970 --> 00:16:29.570
to publish.
00:16:30.559 --> 00:16:32.149
it's mathematically beautiful.
00:16:32.177 --> 00:16:33.017
I don't question this.
00:16:33.017 --> 00:16:36.106
Maybe some people a really good at that.
00:16:36.136 --> 00:16:46.126
Maybe better than many of us, but it's very little use dilutes the literature in the field and he also has very little use for engineers.
00:16:46.126 --> 00:16:54.662
And again, I'm not arguing that all literature should be a linked to the practical ward, but you should not mislead at least engineers.
00:16:54.662 --> 00:16:57.792
You should not lead them into doing, design mistakes.
00:16:57.892 --> 00:17:06.065
And this is unfortunately what I see because this literature is so big of those kinds of papers, trying to modeling panic or contagion or, , whatever.
00:17:07.099 --> 00:17:22.473
Let's say behavior that has been, uh, deconstructed and, dismounted in the literature, in the field of social psychology or similar fields it is actually warring at this point because we, we are not able to stop this, , and they cite each other and, they continue doing this.
00:17:22.473 --> 00:17:23.403
I can make an example.
00:17:23.463 --> 00:17:23.854
example.
00:17:24.497 --> 00:17:27.076
A nice chat at some point with an applied mathematician.
00:17:27.136 --> 00:17:30.586
And he told me something that I always suspect is a crowd modeler.
00:17:31.037 --> 00:17:33.527
And the Emiliano Christiana is from Rome.
00:17:33.586 --> 00:17:39.334
And anytime, you know, there are some people that really fall in love, with certain mathematical equations and they will
00:17:39.604 --> 00:17:39.693
Yeah.
00:17:40.019 --> 00:17:42.624
in our field to squeeze those everywhere.
00:17:42.864 --> 00:17:45.743
So they say, oh, I can use this also for crowd dynamics and.
00:17:46.223 --> 00:17:51.413
And, for those that instead, try to link this to the real world and, and have a use of this.
00:17:51.413 --> 00:17:52.554
This is painful.
00:17:53.064 --> 00:17:55.614
is painful because you see such a waste of resources.
00:17:55.614 --> 00:17:56.453
So these people could be
00:17:56.479 --> 00:17:56.689
Yeah.
00:17:56.693 --> 00:18:04.584
brilliant solving, certain problems with evacuation modeling, but they putting said so much emphasis on something else that is not needed.
00:18:04.584 --> 00:18:06.534
So that's what we both were saying.
00:18:06.534 --> 00:18:06.923
Okay.
00:18:06.923 --> 00:18:10.467
We wished that we will have, the right effort.
00:18:10.957 --> 00:18:13.696
Putting the right problems, in our community.
00:18:13.988 --> 00:18:18.469
I think this dilution is not only, typical for evacuation science.
00:18:18.699 --> 00:18:33.398
I see that a lot in tunneling example, there's a group of researchers who will, determine that back layering distance or critical velocity, The, the fourth, , significant digit when, as an engineer is just double it And then make it safe.
00:18:33.638 --> 00:18:35.243
You don't really care that much.
00:18:35.506 --> 00:18:40.096
and as as you mentioned, this creates this powerful ecosystem.
00:18:40.695 --> 00:18:47.655
I mean, when it's just one paper that does that or one researcher who does it it can be an error It can be in mistake.
00:18:48.306 --> 00:19:01.169
It can be just a honest misjudgment of a scientist, but when it forms an ecosystem where this is continuously repeated, repeated, repeated, you Start building on the parallel paradigm.
00:19:01.219 --> 00:19:03.642
You need to build science you need to have paradigms.
00:19:04.061 --> 00:19:15.823
And if, in your case modeling, assuming some certain behaviors some certain extreme behaviors that do not have, Proof in really evacuation studies, a real fires.
00:19:16.212 --> 00:19:18.163
if that suddenly becomes a paradigm for.
00:19:18.163 --> 00:19:21.522
a part of your field, it, this despite the field is genuinely lost.
00:19:21.762 --> 00:19:33.353
And as you mentioned, that is waste of resources that is waste of human time that this waste of work sometimes, brilliant scientists who are really good at what they're doing.
00:19:33.353 --> 00:19:36.858
For example, applying this mathematical problems to solve something.
00:19:37.278 --> 00:19:39.788
And we are not a big field.
00:19:39.788 --> 00:19:41.229
We don't have people to lose.
00:19:41.229 --> 00:19:43.239
We we need all hands on deck.
00:19:43.239 --> 00:19:46.239
solving important problems, of our discipline.
00:19:46.989 --> 00:19:54.638
Do you think may be sometimes entering the spiral can be just a conscious choice because it may be somewhat easier or
00:19:54.979 --> 00:19:57.308
I honestly don't think so.
00:19:57.308 --> 00:20:19.412
I think it's generally a genuine lack of understanding of the literature, which leads to basically sticking to a certain type of terminology and a certain type of assumptions that let's say if we are split and we are, , let's say a percentage of people that claim that we should modeling panic, a contagion and so on, and let's say.
00:20:20.025 --> 00:20:26.204
Part of the community is trying to explain over and over and over in the last 30, 40 years.
00:20:26.204 --> 00:20:33.739
I mean, this morning we were discussing it that the first papers discussing panic misconceptions, all the come from the seventies or things like this.
00:20:33.739 --> 00:20:35.868
So it's still that we're battling.
00:20:36.463 --> 00:20:45.763
I think there is a genuinely honest problem with the community is not able to intervene to spread this misconception.
00:20:45.763 --> 00:20:59.104
And in the, in our case, this is also very much fed by media he's fed, by, you know, everything that you watch outside the scientific world because that's the problem that we have as well, maybe other domain, , modeling of fires.
00:20:59.814 --> 00:21:12.614
It is not such a big problem, in our domain, the thing is that when you turn on the TV or open a newspaper online, always this tendency to have a sensational titles and so on.
00:21:12.614 --> 00:21:15.463
So they want to use kind of extreme.
00:21:15.963 --> 00:21:25.701
wording and depict this extreme behavior, even if they didn't happen because their goal is often to sell newspapers rather than, actually, telling what, what happened.
00:21:25.701 --> 00:21:34.761
But I said, I don't think there is a need an agenda in a way it's more like a genuine mistake made by a portion of the community that is.
00:21:35.214 --> 00:21:47.578
comes another world I mentioned the world of applied mathematics, but it can be applied physics or anything that is not to the core literature or in the field of psychology, social psychology.
00:21:47.578 --> 00:21:52.182
And so on that explained us this mechanism, of human behavior and fire, work.
00:21:52.598 --> 00:21:55.868
so this is what I think is the, is the problem nowadays.
00:21:55.898 --> 00:22:16.994
And to be honest with you, I'm even more worried now with the new trends of science and machine learning and so on, because these are fantastic tools, but they, often start from another premise that we don't want to understand the fundamental mechanisms, we want to identify patterns in what happens.
00:22:17.894 --> 00:22:24.285
And often this leads to the blind trusting the data, which is the biggest problem that we have in this case.
00:22:24.674 --> 00:22:43.809
And which I think we should be very careful about because there is a huge risk that we will end up now with a whole bunch of new literature, , in the domain of evacuation using these kind of data science approaches with machine learning and so on, may really start from data that have problems.
00:22:43.809 --> 00:22:48.730
And instead they take them as good by default and come up with wrong conclusions.
00:22:48.759 --> 00:22:51.190
And as I say, don't get me wrong because.
00:22:51.775 --> 00:22:57.894
Big, a big enthusiast, myself of this kind of data science approach and machine learning.
00:22:57.894 --> 00:23:08.679
But I just have to put down a warning there for our field specifically because there is this risk, to data, and get, start getting models that.
00:23:09.141 --> 00:23:18.974
Down conclusions that do not make too much sense just because we are not putting efforts in understanding the fundamentals what is happening, but just trying to identify patterns into what happens
00:23:19.248 --> 00:23:37.476
so in my field using machine learning or artificial and any type of big data tool actually is, let's say straightforward because I'm working with fundamental physics, the conservation of energy in the mass is always the same the way how, I don't know, pressure or density changes with temperature.
00:23:37.776 --> 00:23:46.429
I can describe this with an equation, even if I have very, very complex relation between the group of variables and my outcome.
00:23:46.438 --> 00:24:01.173
It is in the physical low and here by data, I assume you mean the common tools for evacuation modelings, which would be surveys, which would be data collected from, evacuation drills and real incidents.
00:24:01.564 --> 00:24:18.309
It would be a mathematical data describing the movement of people, but mathematical data cannot describe the behavior of people or their choices, or at least not in such a straightforward way as I can describe the motion of smoke or heat transfer through a partition in the building.
00:24:18.970 --> 00:24:39.294
And so you are really like Your data by assumption has this level of There is that you first need to understand that the variability in your data set and you first need to understand what your data represents before you drop it into a black book seeking for hidden relations.
00:24:39.324 --> 00:24:40.463
Is that what you meant?
00:24:40.617 --> 00:24:42.327
I think this is exactly what I mean.
00:24:42.357 --> 00:24:45.560
And sometime I make decent example.
00:24:45.560 --> 00:25:11.123
When I talk about this, I don't know how many of you are chess players, but you've probably heard the story of alpha zero, this super software that has been built learning from millions of chess games and the game and actually having one of the main grand master number one in one ofMagnus Carlssen and learning the software that use this kind of, algorithm carying just about the final outcome, not about the chest theories on everything that we w we know from the theory.
00:25:11.722 --> 00:25:19.643
in that context, this works really well because assumptions to start with, so the movement of the pieces are what they are.
00:25:19.643 --> 00:25:23.512
So there is no, let's say a M B
00:25:23.719 --> 00:25:24.439
We showed them
00:25:24.893 --> 00:25:25.673
a straight line.
00:25:25.702 --> 00:25:26.633
They move diagonally.
00:25:26.663 --> 00:25:27.143
That's it.
00:25:27.563 --> 00:25:28.853
So instead.
00:25:28.915 --> 00:25:44.339
What we see in our field is that there are also aspects and assumptions that are really, questionable sometimes, and are they're really object of, debates how and why certain behavior, occur.
00:25:44.359 --> 00:25:52.380
and also they are depending on the local conditions that you have, because if you have a very large data set, I mean, I spoke with a very brilliant researchers.
00:25:52.819 --> 00:25:56.063
With the massive data sets of, pedestrian movement.
00:25:56.063 --> 00:26:05.123
Like for instance, I think that the group that has the largest collection of data sets on this, it's a university of Eindhoven with Alessandra Corvette and Federico Tasco.
00:26:05.153 --> 00:26:09.772
These guys, they put sensors in train station, they collect millions of trajectories.
00:26:09.772 --> 00:26:12.353
So it's really fascinating what they do.
00:26:12.962 --> 00:26:14.992
But imagine even with this kind of huge.
00:26:15.593 --> 00:26:23.903
This will still be a very specific population in a very specific context, in a very specific environment.
00:26:23.903 --> 00:26:28.613
So to generalize findings, eh, he needs another step he needs.
00:26:28.913 --> 00:26:31.403
Like I, I see those two things as complimentary.
00:26:31.432 --> 00:26:42.182
I'm really happy that these heifers are hip are happening I think they're really good, but we cannot just rely on those models try to identify patterns in these big data.
00:26:42.482 --> 00:26:43.893
We also need to try to dig up.
00:26:44.698 --> 00:26:47.337
Into understanding what are the fundamental processes?
00:26:47.904 --> 00:26:50.484
And theories that can explain we see.
00:26:50.484 --> 00:26:54.934
That's why hope that many of the people that work with these methods probably wouldn't agree with me.
00:26:55.325 --> 00:27:00.125
don't think that those data science methods should substitute what we have been doing so far.
00:27:00.125 --> 00:27:03.018
They should compliment it, but we cannot have this a subsidy.
00:27:03.434 --> 00:27:10.881
our current, theoretical methods, when it comes to those types of data, for which we have a lot of uncertainty to begin with.
00:27:10.911 --> 00:27:15.471
And as I say, we may argue for hours, what domains of fire science.
00:27:15.471 --> 00:27:18.171
They have those kinds of uncertainties, which ones they have not.
00:27:18.621 --> 00:27:23.540
heard a lot of discussion for instance, and I'm not expert in that, but I've
00:27:23.652 --> 00:27:24.731
I've heard a little discussion.
00:27:24.731 --> 00:27:25.961
on Why
00:27:26.060 --> 00:27:28.941
use deterministic models a lot infire modelling than, much
00:27:28.991 --> 00:27:29.412
watch more
00:27:29.931 --> 00:27:30.351
models.
00:27:30.381 --> 00:27:31.141
And there,
00:27:31.192 --> 00:27:31.971
there, you have a
00:27:33.330 --> 00:27:39.421
model, mostly because you have a physical phenomenon, but even in that physical phenomenon, there are so many things that can impact it.
00:27:39.421 --> 00:27:44.891
So many boundary conditions and, and all sorts of other variables that we cannot fully control.
00:27:45.380 --> 00:27:57.839
In our subfield of evacuation this is evident to me that we are not yet at the stage of science understanding of which we can completely control,, and fully understand what leads to certain behavior.
00:27:58.079 --> 00:28:03.279
So we need to be really cautious when we use, data science approaches, because they often.
00:28:04.105 --> 00:28:05.305
Take the data as they are.
00:28:05.305 --> 00:28:06.295
They don't question them.
00:28:06.295 --> 00:28:06.595
So
00:28:07.085 --> 00:28:07.654
the next day.
00:28:07.704 --> 00:28:12.625
and I think there are groups that are trying to do this in also to try to understand how
00:28:12.644 --> 00:28:12.765
Oh,
00:28:12.775 --> 00:28:24.230
treat these kinds of data in order to understand, because sensor, any device, anything can have fault or can, there can be a inner problem with the data so that's why I think we should be very cautious with this.
00:28:24.230 --> 00:28:34.881
And, and maybe I don't want to sound over negative, but I think these are very good tools, but they should be seen as complimentary, not as a substitution to everything that we've been doing so far regarding fundamental theories.
00:28:35.205 --> 00:28:36.756
I have a challenging thought for you.
00:28:37.162 --> 00:28:43.967
if I look Well, in the years of development of the tools we use to predict, evacuation processes and buildings.
00:28:44.596 --> 00:29:04.663
whether you go back to like seventies, eighties, you use graphs to solve it on a piece of paper, or you use the first spreadsheets to calculate it in computer, or you start going into computer models which were it's a glorified spreadsheets, or you go into models that model that go with, you know, movement trajectories and calculate parts, social forces, and so on.
00:29:04.933 --> 00:29:06.074
Or even you jump into.
00:29:06.523 --> 00:29:14.134
machine learning, You still have the exact same constraint related to your boundary of the human decision.
00:29:14.134 --> 00:29:14.554
process.
00:29:14.584 --> 00:29:24.116
like in every single of these tools, you are constrained by the exact same limitation of not knowing the, principles behind human behaviors.
00:29:24.116 --> 00:29:37.888
So maybe if that's the case for all from the simplest, tool to the advance or a modern tool you have, maybe that's a Place where you should push your heard of evacuation scientists to solve.
00:29:37.888 --> 00:29:39.568
maybe we need a standardized model.
00:29:39.568 --> 00:29:46.318
of a human behavior that could be just applied in research instead of, trying to figure it out, from scratch.
00:29:47.046 --> 00:29:55.086
what would be this, if you had the power to just solve it, instantly which path would you, would you go and, what would you take to deliver that to the community?
00:29:55.599 --> 00:30:03.910
I mean, there have been efforts in this direction, at least to understand some of the key variables and a key mechanism.
00:30:03.910 --> 00:30:05.049
So you wouldn't be able to fire.
00:30:05.119 --> 00:30:10.039
And as I said, I think exercise that all models.
00:30:10.559 --> 00:30:22.426
Do is to take indeed what I mentioned before this behavioral statements, are a list of statements that describe what happens with humans, during a fire emergency what do they do?
00:30:22.967 --> 00:30:26.176
And try to violate their assumptions again, against those.
00:30:26.176 --> 00:30:29.267
So to see there are assumptions, hold against this
00:30:29.353 --> 00:30:29.863
That's good.
00:30:30.196 --> 00:30:34.656
now as a researcher and also fortunately, as mentors of researchers, what they tell them.
00:30:35.372 --> 00:30:45.048
, every time that you write a research paper in which you are investigating a specific population or a specific, scenario and so on, to create these behavioral statements.
00:30:45.048 --> 00:30:48.979
So try to create simple statement that concisely.
00:30:49.598 --> 00:31:00.699
Describe, what actually happened in that given situation so that a modeler can take this statement and use it as a benchmark the assumptions that are used in the model.
00:31:00.699 --> 00:31:13.388
Because as I said, we do this exercise, if we take this knowledge that we have nowadays, and we compare it, with models, we will definitely find some models that violate through their assumptions, some of these statements.
00:31:14.618 --> 00:31:23.108
as I say, the trends that I see, especially in the commercial world, and that's a, a bit strange because I would have expected more researchers to keep up with.
00:31:23.858 --> 00:31:25.608
Actually see it much more in the commercial world.
00:31:26.419 --> 00:31:38.205
model developers in the evacuation world are trying to actually give flexibility to their model so that they, at least you can make sure that you can, calibrate the model in a way that doesn't violate certain assumptions, that you have.
00:31:38.236 --> 00:31:48.301
But as I said, in, in the research world unfortunately, it happens often that want to reinvent the wheel from scratch and that's not probably the right approach.
00:31:48.332 --> 00:32:00.625
I mean, if I will never imagistic and decide what to do with the research funding in the vacation world, in the world, I would basically put 90% of my money on data collection and theory and
00:32:00.671 --> 00:32:00.924
The,
00:32:00.924 --> 00:32:02.625
the modeling be done by the
00:32:03.000 --> 00:32:03.151
um,
00:32:03.414 --> 00:32:05.355
model developers in companies.
00:32:05.355 --> 00:32:08.744
They probably are much better writing codes than all of us.
00:32:09.055 --> 00:32:11.515
is at least how I will, how we'll do it,
00:32:11.565 --> 00:32:17.625
so these rules could be, Or can be applied as some sort of sanity check if you're modeling makes sense.
00:32:17.625 --> 00:32:17.894
Right?
00:32:18.445 --> 00:32:19.405
Yeah, that's the idea.
00:32:19.405 --> 00:32:34.231
So I mean to, try to benchmark the assumptions that you use against athose simple rules and, you know, there have been simple rules defined in different domains of the evacuation world that have been these, be able to statements that come from our own community for fire safety science.
00:32:34.821 --> 00:32:44.796
for instance, there are a lot of, efforts done in the pedestrian, evacuation dynamics community, instance, in identifying, rules of, interactions between people in a crowd.
00:32:45.155 --> 00:32:50.945
So at a more microscopic level, see, how does a lane of people get formed?
00:32:51.500 --> 00:32:56.510
Brilliant researching this or how actually queuing happens.
00:32:56.550 --> 00:33:07.090
So, trying to extract those rules from the individual papers, boil them down in simple statements and then benchmark the models that we have out there against those.
00:33:07.510 --> 00:33:08.980
see, does this get violated?
00:33:09.520 --> 00:33:13.121
To begin with, is this inbuilt in the defaults of the models?
00:33:13.721 --> 00:33:19.181
we modify the default so that at least we can tune the model in a way that these assumptions are not violated any more?
00:33:19.181 --> 00:33:24.851
So this is the kind of benchmarking work that I would like to see more and more.
00:33:24.851 --> 00:33:30.590
And I mean, it's all linked to the efforts that have been done over the years in and validating, evacuation models.
00:33:30.590 --> 00:33:31.131
You know, this
00:33:31.186 --> 00:33:31.457
Y,
00:33:31.490 --> 00:33:31.911
of my.
00:33:32.011 --> 00:33:38.400
main, life mission as a researcher to develop standardized testing for over evacuation models.
00:33:38.849 --> 00:33:39.450
But as I said,
00:33:39.480 --> 00:33:50.279
as I said, we need to have another step the community trying to make their simple enough to be directly implemented into models.
00:33:50.509 --> 00:33:52.640
I have one more challenging.
00:33:52.640 --> 00:33:53.750
One asset.
00:33:53.779 --> 00:34:30.114
so I think in most of these approaches, we focus very much on the behavior of a single agent or a single person, that evacuate, but Do we truly need this microscopic approach to simulate the population, you know, to see large crowd patterns to see queuing, is, this the only way, because we started this with the issues with papers and his part of the science that goes in a slightly wrong way, because they misunderstand some of the assumptions and, to do things that are putting a lot of effort in researching things.
00:34:30.114 --> 00:34:31.344
that makes no make no sense.
00:34:31.463 --> 00:34:32.244
basically.
00:34:32.664 --> 00:34:43.789
so maybe if we could break the product that you modeled an agent, maybe there are waste, to skip that individual behavior and just focus on like group behavior or something.
00:34:43.789 --> 00:34:44.239
bigger.
00:34:44.719 --> 00:34:47.059
And how do you do wild land interface, fire?
00:34:47.059 --> 00:34:49.550
Do you, do still stick to agent or.
00:34:49.925 --> 00:34:53.543
I think this is a, another very good point of discussion.
00:34:53.543 --> 00:35:06.353
I had several discussions about this with different researchers that work in different domain, both in the WUI fire world and in the crowd, evacuation world, I think there is not a type of modeling approach that works for everything.
00:35:06.523 --> 00:35:07.643
I think it really depends.
00:35:08.527 --> 00:35:22.284
Problem that you need to solve because some cases it is true to, you could actually use a very simple, say flow calculation or a very simple microscopic model and get a fairly decent approximation of the result that you need.
00:35:22.827 --> 00:35:34.001
in some other contexts is actually probably not enough, especially when you, the impact of an individual, a behavior or a small group of individuals can, uh, can be huge on your total evacuation time.
00:35:34.391 --> 00:35:35.201
Then you can adjust.
00:35:35.686 --> 00:35:42.425
The big picture you may get these diluted and maybe let's say overlook certain aspects.
00:35:42.425 --> 00:35:50.706
So discussion came up a lot, for instance, when it comes to WUI fires because there, there is even a lot of people arguing that we don't even have to
00:35:50.942 --> 00:35:51.211
Yes.
00:35:51.485 --> 00:35:57.496
pedestrian execution modeling within with our modeling because of the scale of the traffic.
00:35:57.496 --> 00:36:00.585
When you have scenarios, we, traffic evacuation is much bigger.
00:36:00.925 --> 00:36:04.992
but again, I will not the answer like yes we need or no, we don't.
00:36:05.353 --> 00:36:10.902
The answer is, it really depends on which scenario you're looking at and what conditions you're looking at.
00:36:10.902 --> 00:36:12.552
Both for buildings and WUI fires.
00:36:12.936 --> 00:36:16.385
we should be able to have in the available.
00:36:16.385 --> 00:36:23.967
a pool of tools then select the most appropriate one, given problems that we need to solve in the design issues that we have.
00:36:23.967 --> 00:36:29.518
So will not argue, I will not recommend to just abandon a microscopic modeling.
00:36:29.547 --> 00:36:31.197
That's definitely not a good idea.
00:36:31.228 --> 00:36:36.068
Uh, personally, I think, nor abandoned macroscopic modeling, because that is also useful
00:36:36.079 --> 00:36:36.168
Okay.
00:36:36.217 --> 00:36:38.858
many instances, but more like choosing.
00:36:39.413 --> 00:36:45.882
What is the right path that we need to take, depending on what applications we have and have those two tracks developing in parallel.
00:36:45.882 --> 00:36:58.867
So maybe microscopic models can learn something from microscopic models and vice versa, , because you know, there've been also efforts trying to combine those and the using,, hybrid, approaches and so on, but that's not a trivial,
00:36:59.389 --> 00:37:02.980
would have to decide when a person changes from, , a person into a population.
00:37:03.150 --> 00:37:13.945
It's this, this sharp edge when If you would agree that that's some point that the population scale, these individual decisions have less and less impact because essentially they become statistics.
00:37:14.432 --> 00:37:16.889
if you care about the movement of a 1 million people.
00:37:17.369 --> 00:37:20.869
the decision of of a single person ends up being a statistics that's it.
00:37:21.199 --> 00:37:22.400
So, finding , this.
00:37:22.436 --> 00:37:25.485
Edge between them and applying correct approaches.
00:37:25.485 --> 00:37:49.516
to each, maybe an important, aspect of modeling overall And, I think in the same way, it's when you go from modeling agent movement into agent behavior, it's also a, a, shop interface where you literally stopped doing something very simple and jumped into a field where more complicated things, dictate the outcome.
00:37:49.757 --> 00:38:05.708
and like, honestly, from your experience, do you think in I don't know, next five 10 years we will have reliable behavioral models to use in our modeling, like I go today, I start up computer software of my choice to model.
00:38:05.708 --> 00:38:10.655
my, building, I pop in the pre evacuation distribution times and, I run the simulations.
00:38:10.887 --> 00:38:19.315
Will I be able in like five to 10 years, be able to just put default human behavior, model and then just run the same simulations without this.
00:38:20.083 --> 00:38:29.465
I think in five years, no, probably I don't want to be over optimistic because that is something that we have been looking around already for quite some time.
00:38:29.496 --> 00:38:41.856
And, and we are digging into this, but I will argue the tools that we have today and as mentioned several of them, they are becoming more and more flexible in a way that have as users, as solid understanding.
00:38:42.521 --> 00:38:47.170
Of what are the scenarios and the behaviors that we want to represent.
00:38:47.740 --> 00:38:51.221
can, we actually have the design tools to do our design nowadays.
00:38:51.221 --> 00:39:01.621
So is missing here is a more research towards of everything that has to do with those outliers scenarios that we have.
00:39:02.786 --> 00:39:14.936
Understanding and control on because we know that in certain situations they may be a small group or of people doing a certain behavior that might dramatically affect our results.
00:39:14.965 --> 00:39:20.353
And, you know, when we design a performance based deisgn fire engineering, we actually look at RSET.
00:39:20.373 --> 00:39:25.036
So we look at the actual, , tail of, our evacuation time curves.
00:39:25.255 --> 00:39:27.005
And that's where I would like to.
00:39:27.367 --> 00:39:32.847
more research too, because now we can do it, but we have a, quite a large uncertainty in this.
00:39:32.847 --> 00:39:41.050
So I hope, and I really think that we will doing 10 years from now is to basically work more on, making a small.
00:39:41.429 --> 00:39:45.940
gap what is the result that we expect and the uncertainty that we
00:39:46.164 --> 00:39:46.885
Amazingly.
00:39:47.079 --> 00:39:55.960
to basically, uh, have anRSET because, you know, when we work with RSET ASET, sometimes actually we recommend to have these probabilistic approaches to.
00:39:56.474 --> 00:40:00.945
To make sure that in 10 years from now, this uncertainty is reduced.
00:40:00.974 --> 00:40:08.664
So span of possible results become smaller, which means that we have more confidence in our, RSET when we do our design.
00:40:08.675 --> 00:40:11.695
So this is what I see happening 10 years from now.
00:40:11.724 --> 00:40:18.213
I don't think we're going to be able to pin down to one RSET for each very specific scenario because also.
00:40:18.213 --> 00:40:22.735
That's not really how we work and how are.
00:40:22.764 --> 00:40:39.934
It's very, there's so many variables that can play a role that it's very hard to isolate them all, especially in the timeframe of five or 10 years from now, but we can improve our understanding of this uncertainty and decrease that uncertainty that we actually have a better understanding of what these rset.
00:40:39.954 --> 00:40:42.054
This requires safety egress time is.
00:40:42.210 --> 00:40:52.664
I just can't stop myself from asking you for predictions for the future and for the new listeners of the podcast, there's like a whole episode with two Italians rambling on how the future of evacuation.
00:40:52.664 --> 00:40:53.594
modeling will look like.
00:40:53.594 --> 00:41:02.324
And I I highly recommend that, , the out it was a great discussion and I would also love to recommend your episode with Erica who.
00:41:02.949 --> 00:41:07.056
Explains how she's modeling behavior in wild land fires.
00:41:07.056 --> 00:41:09.695
And that is, that is truly beautiful.
00:41:09.706 --> 00:41:15.635
on the, how this aspects of human behavior, are being modeled and what it takes to actually model them.
00:41:15.876 --> 00:41:19.177
Maybe not the buildings outside of buildings, but still it's, Similar problem.
00:41:19.177 --> 00:41:24.088
And I absolutely love how she presented it and it really, shows the complexity of the problem.
00:41:24.168 --> 00:41:29.875
And I think through a resources like this, true research like that we, truly bring.
00:41:29.905 --> 00:41:33.144
more people closer to understanding.
00:41:33.880 --> 00:41:36.130
What is the big problem in here?
00:41:36.130 --> 00:41:37.960
what is the big picture in here?
00:41:38.409 --> 00:41:52.465
And if to understand that they have to move back to fundamentals and by moving back, to fundamentals, they move away from doing this misjudge conceptions that, you can simplify a human behavior into, , a phenomenon of panic Or whatever else.
00:41:52.465 --> 00:41:55.076
And, uh, I guess th that could be like a good.
00:41:55.076 --> 00:41:59.333
Summary on where should we go educate people on the complexity?
00:41:59.333 --> 00:42:08.637
And what it really means, to, to model behavior, to stop this overflow of bad science that in the end influences our discipline.
00:42:09.152 --> 00:42:09.534
Correct.
00:42:09.563 --> 00:42:11.784
And I think this is a good take home message.
00:42:11.784 --> 00:42:22.456
So, , if you are, a user of models even if you are a researcher, back and start questioning, the assumptions, both that the model use.
00:42:23.047 --> 00:42:39.873
And that you are using because this is one of the key aspects that can lead, to mistakes and problems in your simulation world So I think, these should be the take home message, when it comes to, representation of such a complex problem, like human behavior and fire.
00:42:39.994 --> 00:42:40.498
Fantastic.
00:42:40.498 --> 00:42:41.900
that's a great, summary.
00:42:41.900 --> 00:42:52.978
I I link all the resources into show notes, and I hope we've helped some scientists to get on the correct path and others to, to help us.
00:42:53.059 --> 00:43:03.452
, fight or battle of, bad science, thank you very much Enrico for coming to the show for the second time, it was a huge pleasure to have you here.
00:43:03.452 --> 00:43:08.222
And, uh, I have a feeling we will see each other, not so far from now.
00:43:08.222 --> 00:43:10.893
Again, maybe touching on the RSET concert.
00:43:10.922 --> 00:43:11.193
Oh man.
00:43:11.193 --> 00:43:13.233
That would be, that would be fun.
00:43:13.628 --> 00:43:13.996
thanks.
00:43:13.996 --> 00:43:20.547
Wojciech was very nice to be back and, thanks a lot for all the efforts and time you put into this podcast because it's really fantastic.
00:43:20.567 --> 00:43:21.646
We really needed it.
00:43:21.829 --> 00:43:22.400
Oh, thank you.
00:43:22.650 --> 00:43:23.420
I'm so happy.
00:43:23.420 --> 00:43:24.739
I didn't know what, I didn't know.
00:43:24.739 --> 00:43:25.309
we needed it.
00:43:25.340 --> 00:43:27.469
until I started it and now I just can't stop.
00:43:27.889 --> 00:43:29.179
It's really cool experience.
00:43:29.210 --> 00:43:29.730
Thank you.
00:43:30.626 --> 00:43:30.987
Thanks.
00:43:30.987 --> 00:43:31.485
Bye-bye.
00:43:32.166 --> 00:43:34.746
I hope you've enjoyed this episode.
00:43:35.317 --> 00:43:45.067
And if there's one takeaway that you can take from it, I guess a sanity check approach for testing your evacuation model would be the thing to take.
00:43:45.577 --> 00:43:50.376
Enrico mentioned the paper and I found it, the paper is
00:43:50.376 --> 00:43:57.795
Guidance for the model developer on representing human behavior in egress models and it was published in Fire and Technology journal.
00:43:58.505 --> 00:44:02.255
It's authored by a Gwynne, Hulse and Kinsey.
00:44:02.735 --> 00:44:04.025
And I link.
00:44:04.445 --> 00:44:06.726
It in the episode description, obviously.
00:44:07.436 --> 00:44:10.715
Enrico mentioned the paper contains statements.
00:44:10.746 --> 00:44:11.976
And in fact it does.
00:44:12.260 --> 00:44:18.356
Statements that, are, let's say the things that we know about the behavior of people when they evacuate.
00:44:18.925 --> 00:44:24.626
And this can be used to cross check your analysis, whether you break this statements or not.
00:44:24.760 --> 00:44:31.420
And if you do well, then most likely there is some error or misconception or the model is used in an incorrect way.
00:44:31.960 --> 00:44:32.380
And.
00:44:32.380 --> 00:44:36.090
As we don't have yet the perfect models as we don't.
00:44:36.090 --> 00:44:39.498
Have you had the ability to just easily model the.
00:44:39.748 --> 00:44:41.458
Evacuation behavior of people.
00:44:41.481 --> 00:44:47.485
Actually cross-checking our simulations with such a sanity check is, sounds like a pretty powerful.
00:44:47.490 --> 00:44:49.771
Tool that can be used to make sure that.
00:44:49.791 --> 00:44:58.657
And modeling is of high quality, and I would recommend it to engineers and I would highly recommend it to scientists who research in this field.
00:44:59.137 --> 00:45:03.847
And as mentioned in the start of the episode, I would also highly recommend listening to that.
00:45:04.427 --> 00:45:06.057
IAFSS webinar.
00:45:06.228 --> 00:45:10.717
There was a channel on YouTube, uh, with, these webinars did this one was the third one.
00:45:11.257 --> 00:45:12.128
And they're all great.
00:45:12.128 --> 00:45:13.833
So I hope this is another.
00:45:13.833 --> 00:45:16.099
resource that is interesting to you.
00:45:16.114 --> 00:45:18.367
And, I hope you'll reach out for, for that.
00:45:18.998 --> 00:45:20.918
Thank you for being here with me.
00:45:21.338 --> 00:45:24.188
Thank you for another week of podcasting.
00:45:24.697 --> 00:45:27.697
I look forward to the next week.
00:45:27.728 --> 00:45:29.815
So see you again next Wednesday.
00:45:29.844 --> 00:45:30.175
Thank you.
00:45:30.175 --> 00:45:30.565
Bye.
