Browsing by Author "Ehtamo, Harri, Prof., Aalto University, Department of Mathematics and Systems Analysis, Finland"

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    Cooperation, commitment, and other-regarding behavior in duopoly games
    (Aalto University, 2016) Leppänen, Ilkka; Hämäläinen, Raimo P., Prof., Aalto University, Department of Mathematics and Systems Analysis, Finland; Matematiikan ja systeemianalyysin laitos; Department of Mathematics and Systems Analysis; Systems Analysis Laboratory; Perustieteiden korkeakoulu; School of Science; Hämäläinen, Raimo P., Prof., Aalto University, Department of Mathematics and Systems Analysis, Finland; Ehtamo, Harri, Prof., Aalto University, Department of Mathematics and Systems Analysis, Finland
    Firms and other economic actors often forgo own advantage and instead display other-regarding preferences. This Dissertation studies the reasons for this behavior in the context of duopoly games using laboratory experiments and analytical models. We develop new results concerning behavior in duopoly games under imperfect commitment and incomplete information and when interactions are repeated. We also show that the interplay of conjectural variations and other-regarding preferences has novel implications for behavior in strategic interactions. The behavioral methods used in the Dissertation include standard laboratory experiments as well as psychophysiological methods that allow correlating laboratory behavior to activation of the autonomic nervous and skeletomuscular systems. The analytical methods include evolutionary game theoretic models where evolutionarily stable strategies are sought in finite and infinite size populations and standard game theoretic equilibrium analysis where conditions for uniqueness of subgame-perfect equilibria are sought in extensive form games of incomplete information. The objectives of the Dissertation are to provide experimental evidence on the role of costless commitment via cheap talk, private information, and emotions in cooperation in duopoly games. The objectives are also to provide new analytical results on the interplay of other-regarding behavior and conjectural variations and to examine how asymmetric and stochastic private information affects commitment. The overall theme of the Dissertation is to study how variations on the assumption of own payoff maximization affect players' behavior in duopoly games. The results suggest that imperfect commitments, such as cheap talk announcements and partial commitments, have value in strategic interactions. However, the effects of costless or partial commitments depend on available information about payoffs or marginal costs. We also find that cooperative behavior in repeated duopoly games has an emotional foundation. This is shown e.g. by emotional expressions when observing decision outcomes and by autonomic activity during decision making. Our studies on other-regarding preferences in conjectural variations models suggest that taking these both into account and allowing evolutionary selection leads to novel results. These include the explicit dependence of the evolutionarily stable conjecture on the other-regarding preference parameter and the evolutionary stability of self-regarding behavior with consistent conjectures. We suggest several avenues for future experimental and analytical research.
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    Numerical simulation and optimization models for socio-dynamical features of crowd evacuation
    (Aalto University, 2021) von Schantz, Anton; Ehtamo, Harri, Prof., Aalto University, Department of Mathematics and Systems Analysis, Finland; Matematiikan ja systeemianalyysin laitos; Department of Mathematics and Systems Analysis; Systems Analysis Laboratory; Perustieteiden korkeakoulu; School of Science; Ehtamo, Harri, Prof., Aalto University, Department of Mathematics and Systems Analysis, Finland
    The rapid increase of various mass gatherings and overcrowded festivals pose serious challenges, for example in case of emergency. Computational models may help to address issues related to these socio-physical systems, and in particular evacuating crowds. Physics-inspired self-driven particle models can describe most of the physics of moving crowds. However, there is still a need for comprehensive crowd models that can describe collective crowd effects, starting from individual crowd members' decision-making. In addition to models being able to describe harmful crowd phenomena, they should also prescribe solutions to prevent them. This dissertation concerns the mathematical and computational modeling of an evacuating crowd. The main focus is on studying how individual decision-making causes the harmful physical effects in a bottleneck evacuation. How should rescue guides be used to minimize the evacuation time of a crowd? What is the effect of uncertain crowd movement patterns on the minimum time evacuation plan? A multiagent framework is used to model the crowd. Its members are modeled as agents that interact with each other. The crowd dynamics are described using social force model based on Newtonian dynamics, and the agents' decision-making is described using evolutionary game theory. The model is studied by developing a simulation environment, which is implemented in a high-performance computing cluster. Numerical simulations show that due to the locally-played game, non-monotonous dynamical effects emerge. In a bottleneck congestion, the back of the crowd behaves impatiently. It pushes the agents in front of it, and pressure increases. As a result, arch-like structures form, capable of interrupting the flow and slowing down the evacuation. The arches break down due to fluctuating loads. The results coincide with findings from behavioral and physical evacuation experiments. New mathematical models and algorithms are developed to solve the minimum time crowd evacuation problem with rescue guides. The new methods are based on mathematical optimization, namely, on scenario optimization, genetic algorithms, numerical simulation-based optimization, and bi-objective optimization. Also, worst-case scenarios are accounted for with a risk measure. The solution to the minimum time evacuation problem gives the number of guides, their initial positions, and exit assignments. It is shown that there is a tradeoff between the evacuation plan that performs well across scenarios, and the one that performs well on the worst-case scenario. With enough guides, the uncertainty in the individual and crowd movement patterns is mitigated. This dissertation provides new practical tools for numerical simulation and optimization of dynamical features of crowd evacuation, and hopefully gives ways to prevent fatal accidents in emergencies.
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    Pedestrian Behavior in Evacuations – Simulation Models and Experiments
    (Aalto University, 2014) Heliövaara, Simo; Ehtamo, Harri, Prof., Aalto University, Department of Mathematics and Systems Analysis, Finland; Korhonen, Timo, Dr., VTT Technical Research Centre of Finland; Matematiikan ja systeemianalyysin laitos; Department of Mathematics and Systems Analysis; Systems Analysis Laboratory; Perustieteiden korkeakoulu; School of Science; Ehtamo, Harri, Prof., Aalto University, Department of Mathematics and Systems Analysis, Finland
    In Today's built environment, it is common that large numbers of people gather in buildings. Also evacuations of such large crowds take place frequently all over the world. Standard safety requirements for buildings cannot always ensure safe evacuation of people in such situations, and thus, computational evacuation simulations have become a common practice in building design. The currently available simulation models are able to produce quite realistic movement and egress flows. However, there has not been much focus on modeling evacuees' behavior and decision making, which may significantly affect the outcome of evacuations.  This thesis develops new modeling methods to describe the behavior of pedestrians in evacuation situations. The developed models are implemented in a state-of-the-art simulation software that combines evacuation simulation with fire simulation. The models apply game theory, which is the mathematical framework for describing strategic interaction between individuals.  A novel game theoretic model for occupants' exit route selection is proposed. It describes how evacuees react to the surroundings and other evacuees' actions when deciding which exit to use. Another presented model uses spatial game theory to describe pedestrian behavior and interaction in threatening and congested situations at egress route bottlenecks. The model shows that reasonable behavior by pedestrians may lead to pushing and slow down the egress flow. In addition, a new physical approach for modeling pedestrian counterflow is given.  The thesis also gives the results of an experimental study on pedestrian behavior and decision making in evacuations. It is observed that, even in simple experimental settings, people are often unable to select the fastest egress route. Another interesting finding is that the participants' attempts to cooperate may lead to slower evacuation.  The developed models enable building more realistic tools for evacuation simulation, which help to better assess the safety of different venues. Also, the game theoretic models improve the understanding of how crowd-level movement and phenomena emerge from the behavior and decisions of individual crowd members. The experimental results presented in the thesis provide new insights into human behavior in evacuation situations. The results of the experiments can also be used to validate computational simulation models.
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    People Flow in Buildings – Evacuation Experiments and Modelling of Elevator Passenger Traffic
    (Aalto University, 2015) Kuusinen, Juha-Matti; Siikonen, Marja-Liisa, Dr., KONE Corporation, Finland; Matematiikan ja systeemianalyysin laitos; Department of Mathematics and Systems Analysis; Systems Analysis Laboratory; Perustieteiden korkeakoulu; School of Science; Ehtamo, Harri, Prof., Aalto University, Department of Mathematics and Systems Analysis, Finland
    This dissertation studies people flow in buildings, especially the process of how passengers arrive at elevator lobbies, estimation of elevator passenger traffic, and human behaviour and decision making in evacuations. The arrival process is studied by taking into account, for the first time, that passengers do not always arrive and use elevators individually but rather in batches. The results suggest that the common assumption that individual arrivals follow a Poisson distribution may not hold when the proportion of batch arrivals is large.  To estimate the elevator passenger traffic in a building, new mathematical models and algorithms are developed. The new methods are based on mathematical optimization, namely, linear programming, integer least squares and constraint programming. The results from numerical experiments show that the new approaches satisfy real-time elevator control requirements. In addition, randomized algorithms result in better quality passenger traffic statistics than traditional deterministic algorithms. The dissertation presents also an experimental evacuation study. The results show that people may not be able to select the fastest exit route and that cooperation may slow down the evacuation.  The new estimation models and algorithms presented in this dissertation enable better elevator control and some of them are already being implemented by KONE Corporation. The results also give new insights into the process of how passengers arrive at the elevator lobbies and use elevators and into human behaviour in evacuation situations, which affect elevator and building safety planning.