ACE Models of Endogenous Interactions

Various approaches used in Agent-based Computational Economics (ACE) to model endogenously determined interactions between agents are discussed. This concerns models in which agents not only (learn how to) play some (market or other) game, but also (learn to) decide with whom to do that (or not).Endogenous interaction, Agent-based Computational Economics (ACE)

A model of market-making

The two essential features of a decentralized economy taken into account are, first, that individual agents need some information about other agents in order to meet potential trading partners, which requires some communication or interaction between these agents, and second, that in general agents will face trading uncertainty. We consider trade in a homogeneous commodity. Firms decide upon their effective supplies, and may create their own markets by sending information signals communicating their willingness to sell. Meeting of potential trading partners is arranged in the form of shopping by consumers. The questions to be considered are: How do firms compete in such markets? And what are the properties of an equilibrium? We establish existence conditions for a symmetric Nash equilibrium in the firms' strategies, and analyze its characteristics. The developed framework appears to lend itself well to study many typical phenomena of decentralized economies, such as the emergence of central markets, the role of middlemen, and price-making.Decentralized trade, market--making, communication, trading uncertainty, Leex

An experimental study of adaptive behavior in an oligopolistic market game

We consider an oligopolistic market game, in which the players are competing firm in the same market of a homogeneous consumption good. The consumer side is represented by a fixed demand function. The firms decide how much to produce of a perishable consumption good, and they decide upon a number of information signals to be sent into the population in order to attract customers. Due to the minimal information provided, the players do not have a well--specified model of their environment. Our main objective is to characterize the adaptive behavior of the players in such a situation.Market game, oligopoly, adaptive behavior, learning, Leex

On the Behavior of Proposers in Ultimatum Games

We demonstrate that one should not expect convergence of the proposals to the subgame perfect Nash equilibrium offer in standard ultimatum games. First, imposing strict experimental control of the behavior of the receiving players and focusing on the behavior of the proposers, we show experimentally that proposers do not learn to make the expected-payoff-maximizing offer. Second, considering a range of learning theories (from optimal to boundedly rational), we explain that this is an inherent feature of the learning task faced by the proposers, and we provide some insights into the actual learning behavior of the experimental subjects. This explanation for the lack of convergence to the subgame perfect Nash equilibrium in ultimatum games complements most alternative explanations.Ultimatum game, Non-equilibrium behavior, Laboratory experiment, Multi-armed bandit, Optimal learning, Gittins index, Bounded rationality

On the Behavior of Proposers in Ultimatum Games

We demonstrate that one should not expect convergence of the proposals to the subgame perfect Nash equilibrium offer in standard ultimatum games. First, imposing strict experimental control of the behavior of the receiving players and focusing on the behavior of the proposers, we show experimentally that proposers do not learn to make the expected-payoff-maximizing offer. Second, considering a range of learning theories (from optimal to boundedly rational), we explain that this is an inherent feature of the learning task faced by the proposers, and we provide some insights into the actual learning behavior of the experimental subjects. This explanation for the lack of convergence to the subgame perfect Nash equilibrium in ultimatum games complements most alternative explanations.Ultimatum game, Non-equilibrium behavior, Laboratory experiment, Multi-armed bandit, Optimal learning, Gittins index, Bounded rationality

Schelling's Spatial Proximity Model of Segregation Revisited

Schelling [1969, 1971a, 1971b, 1978] presented a microeconomic model showing how an integrated city could unravel to a rather segregated city, notwithstanding relatively mild assumptions concerning the individual agents' preferences, i.e., no agent preferring the resulting segregation. We examine the robustness of Schelling's model, focusing in particular on its driving force: the individual preferences. We show that even if all individual agents have a strict preference for perfect integration, best-response dynamics will lead to segregation. What is more, we argue that the one-dimensional and two-dimensional versions of Schelling's spatial proximity model are in fact two qualitatively very different models of segregation.Neighborhood segregation, Myopic Nash Equilibria, Best-response dynamics, Markov chain, Limit-behavior.

Imitation of succesful behavior in Cournot markets

In an experimental standard Cournot Oligopoly we test the importance of models of behavior characterized by imitation of succesful behavior. We find that the players appear to the rather reluctant to imitate.Oligopoly, cournot, bounded rationality, spite effect, Leex

On the role of non-equilibrium focal points as coordination devices

Considering a pure coordination game with a large number of equivalent equilibria, we argue, first, that a focal point that is itself not a Nash equilibrium and is Pareto dominated by all Nash equilibria, may attract the players' choices. Second, we argue that such a non-equilibrium focal point may act as an equilibrium selection device that the players use to coordinate on a closely related small subset of Nash equilibria. We present theoretical as well as experimental support for these two new roles of focal points as coordination devices.Coordination game, Focal point, Nash equilibrium, Equilibrium selection, Coordination device, LeeX

Does eeasoning enhance learning?

Utilizing the well-known Ultimatum Game, this note presents the following phenomenon. If we start with simple stimulus-response agents, learning through naive reinforcement, and then grant them some introspective capabilities, we get outcomes that are not closer but farther away from the fully introspective game-theoretic approach. The cause of this is the following: there is an asymmetry in the information that agents can deduce from their experience, and this leads to a bias in their learning process.Ultimatum game, game theory, reasoning, reinforcement learning, Leex

Segregation in Networks.

Schelling (1969, 1971a,b, 1978) considered a simple model with individual agents who only care about the types of people living in their own local neighborhood. The spatial structure was represented by a one- or two-dimensional lattice. Schelling showed that an integrated society will generally unravel into a rather segregated one even though no individual agent strictly prefers this. We make a first step to generalize the spatial proximity model to a proximity model of segregation. That is, we examine models with individual agents who interact ’locally’ in a range of network structures with topological properties that are different from those of regular lattices. Assuming mild preferences about with whom they interact, we study best-response dynamics in random and regular non-directed graphs as well as in small-world and scale-free networks. Our main result is that the system attains levels of segregation that are in line with those reached in the lattice-based spatial proximity model. In other words, mild proximity preferences can explain segregation not just in regular spatial networks but also in more general social networks. Furthermore, segregation levels do not dramatically vary across different network structures. That is, Schelling’s original results seem to be robust also to the structural properties of the network.Spatial proximity model, Social segregation, Schelling, Proximity preferences, Social networks, Undirected graphs, Best-response dynamics.