An Extended Reinforcement Algorithm for Estimation of Human Behaviour in Experimental Congestion Games

The paper reports simulations applied on two similar congestion games: the first is the classical minority game. The second one is an asymmetric variation of the minority game with linear payoff functions. For each game, simulation results based on an extended reinforcement algorithm are compared with real experimental statistics. It is shown that the extension of the reinforcement model is essential for fitting the experimental data and estimating the player\'s behaviour.Congestion Game, Minority Game, Laboratory Experiments, Reinforcement Algorithm, Payoff Sum Model, Game Theory, Experimental Economics

Minority Game - Experiments and Simulations of Traffic Scenarios

This paper reports laboratory experiments and simulations on a minority game. The minority game is the most important example for a classic non-zerosum- game. The game can be applied on different situations with social and economic contests. We chose an elementary traffic scenario, in which subjects had to choose between a road A and a road B. Nine subjects participated in each session. Subjects played 100 rounds and had to choose between one of the roads. The road which the minority of players chose got positive payoffs. We constructed an extended reinforcement model which fits the empirical data.

An Extended Reinforcement Algorithm for Estimation of Human Behaviour in Congestion Games

The paper reports simulations applied on two similar congestion games: the first is the classical minority game. The second one is a asymmetric variation of the minority game with linear payoff functions. For each game simulation results based on an extended reinforcement algorithm are compared with real experimental statistics. It is shown that the extension of the reinforcement model is essential for fitting the experimental data and estimating the players behaviour.congestion game, minority game, laboratory experiments, reinforcement algorithm, payoff sum model

Genetic Action Trees A New Concept for Social and Economic Simulation

Multi-Agent Based Simulation is a branch of Distributed Artificial Intelligence that builds the base for computer simulations which connect the micro and macro level of social and economic scenarios. This paper presents a new method of modelling the formation and change of patterns of action in social systems with the help of Multi-Agent Simulations. The approach is based on two scientific concepts: Genetic Algorithms [Goldberg 1989, Holland 1975] and the theory of Action Trees [Goldman 1971]. Genetic Algorithms were developed following the biological mechanisms of evolution. Action Trees are used in analytic philosophy for the structural description of actions. The theory of Action Trees makes use of the observation of linguistic analysis that through the preposition by a semi-order is induced on a set of actions. Through the application of Genetic Algorithms on the attributes of the actions of an Action Tree an intuitively simple algorithm can be developed with which one can describe the learning behaviour of agents and the changes in action spaces. Using the extremely simplified economic action space, in this paper called “SMALLWORLDâ€, it is shown with the aid of this method how simulated agents react to the qualities and changes of their environment. Thus, one manages to endogenously evoke intuitively comprehensible changes in the agents‘ actions. This way, one can observe in these simulations that the agents move from a barter to a monetary economy because of the higher effectiveness or that they change their behaviour towards actions of fraud.Multi agent system, genetic algorithms, actiontrees, learning, decision making, economic and social behaviour, distributed artificial intelligence

Learning in experimental 2 x 2 games

In this paper, we introduce two new learning models: impulse-matching learning and action-sampling learning. These two models together with the models of self-tuning EWA and reinforcement learning are applied to 12 different 2 x 2 games and their results are compared with the results from experimental data. We test whether the models are capable of replicating the aggregate distribution of behavior, as well as correctly predicting individuals' round-by-round behavior. Our results are two-fold: while the simulations with impulse-matching and action-sampling learning successfully replicate the experimental data on the aggregate level, individual behavior is best described by self-tuning EWA. Nevertheless, impulse-matching learning has the second highest score for the individual data. In addition, only self-tuning EWA and impulse-matching learning lead to better round-by-round predictions than the aggregate frequencies, which means they adjust their predictions correctly over time.learning, 2 x 2 games, Experimental data

Learning in experimental 2×2 games

In this paper we introduce four new learning models: impulse balance learning, impulse matching learning, action-sampling learning, and payoff-sampling learning. With this models and together with the models of self- tuning EWA learning and reinforcement learning, we conduct simulations over 12 different 2×2 games and compare the results with experimental data obtained by Selten & Chmura (2008). Our results are two-fold: While the simulations, especially those with action-sampling learning and impulse matching learning successfully replicate the experimental data on the aggregate, they fail in describing the individual behavior. A simple inertia rule beats the learning models in describing individuals behavior.Learning, Action-sampling, Payo?-sampling, Impulse balance, Impulse matching, Reinforcement, self-tuning EWA, 2×2 games, Experimental data

The Minority of Three-Game: An Experimental and Theoretical Analysis

We report experimental and theoretical results on the minority of three-game where three players have to choose one of two alternatives independently and the most rewarding alternative is the one chosen by a single player. This coordination game has many asymmetric equilibria in pure strategies that are non strict and payoff-asymmetric, and a unique symmetric mixed strategy equilibrium in which each player's behavior is based on the toss of a fair coin. We show that such a straightforward behavior is predicted by Harsanyi and Selten's (1988) equilibrium selection theory as well as alternative solution concepts like impulse balance equilibrium and sampling equilibrium. Our results indicate that participants rely on various decision rules, and that only a quarter of them decide according to the toss of a fair coin. Reinforcement learning is the most successful decision rule as it describes best the behavior of about a third of our participants.Coordination, Minority game, Mixed strategy, Learning models, Experiments

At the Mercy of the Prisoner Next Door. Using an Experimental Measure of Selfishness as a Criminological Tool

Do criminals maximise money? Are criminals more or less selfish than the average subject? Can prisons apply measures that reduce the degree of selfishness of their inmates? Using a tried and tested tool from experimental economics, we cast new light on these old criminological questions. In a standard dictator game, prisoners give a substantial amount, which calls for more refined versions of utility in rational choice theories of crime. Prisoners do not give less than average subjects, not even than subjects from other closely knit communities. This speaks against the idea that people commit crimes because they are excessively selfish. Finally those who receive better marks at prison school give more, as do those who improve their marks over time. This suggests that this correctional intervention also reduces selfishness.experiment, Crime, Prison, Dictator Game, Hurdle Model

The minority of three-game: an experimental and theoretical analysis

We report experimental results on the minority of three-game, where three players choose one of two alternatives and the most rewarding alternative is the one chosen by a single player. This coordination game has many asymmetric equilibria in pure strategies that are non-strict and payoff-asymmetric and a unique symmetric mixed strategy equilibrium in which each player’s behavior is based on the toss of a fair coin. This straightforward behavior is predicted by equilibrium selection, impulse-balance equilibrium, and payoff-sampling equilibrium. Experimental participants rely on various decision rules, and only a quarter of them perfectly randomize

Stationary concepts for experimental 2x2-games

Five stationary concepts for completely mixed 2 x 2-games are experimentally compared: Nash equilibrium, quantal response equilibrium, action-sampling equilibrium, payoff-sampling equilibrium (Martin J. Osborne and Ariel Rubinstein 1998), and impulse balance equilibrium. Experiments on 12 games, 6 constant sum games, and 6 nonconstant sum games were run with 12 independent subject groups for each constant sum game and 6 independent subject groups for each nonconstant sum game. Each independent subject group consisted of four players 1 and four players 2, interacting anonymously over 200 periods with random matching. The comparison of the five theories shows that the order of performance from best to worst is as follows: impulse balance equilibrium, payoff-sampling equilibrium, action-sampling equilibrium, quantal response equilibrium, Nash equilibrium