We examine the force of three types of behavioral dynamics in quantity-setting triopoly experiments: (1) mimicking the successful firm, (2) rules based on following the exemplary firm, and (3) rules based on belief learning. Theoretically, these three types of rules lead to the competitive, the collusive, and the CournotNash outcome, respectively. In the experiment we employ three information treatments, each of which is hypothesized to be conducive to the force of one of the three dynamic rules. To a large extent, the results are consistent with the hypothesized relationships between treatments, behavioral rules, and outcomes.

We introduce a model of stochastic evolutionary game dynamics in finite populations which is similar to the familiar replicator dynamics for infinite populations. Our focus is on the conditions for selection favoring the invasion and/or fixation of new phenotypes. For infinite populations, there are three generic selection scenarios describing evolutionary game dynamics among two strategies. For finite populations, there are eight selection scenarios. For a fixed payoff matrix a number of these scenarios can occur for different population sizes. We discuss several examples with unexpected behavior.

This experiment was designed to test various learning theories in the context of a Cournot oligopoly. We derive theoretical predictions for the learning theories and test these predictions by varying the information given to subjects. The results show that some subjects imitate successful behavior if they have the necessary information, and if they imitate, markets are more competitive. Other subjects follow a best reply process. On the aggregate level we find that more information about demand and cost conditions yields less competitive behavior, while more information about the quantities and profits of other firms yields more competitive behavior.

This paper presents a dynamic model in which agents adjust their decisions in the direction of higher payoffs, subject to random error. This process produces a probability distribution of players' decisions whose evolution over time is determined by the Fokker-Planck equation. The dynamic process is stable for all potential games, a class of payoff structures that includes several widely studied games. In equilibrium, the distributions that determine expected payoffs correspond to the distributions that arise from the logit function applied to those expected payoffs. This "logit equilibrium" forms a stochastic generalization of the Nash equilibrium and provides a possible explanation of anomalous laboratory data.

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We model firms’ output decisions in a repeated duopoly framework, focusing on three interrelated issues: (1) the role of learning in the adjustment process toward equilibrium, (2) the role of organizational structure in the firm’s decision making, and (3) the role of changing environmental conditions on learning and output decisions. We characterize the firm as a type of artificial neural network, which must estimate its optimal output decision based on signals it receives from the economic environment (which influences the demand function). Via simulation analysis we show: (1) how organizations learn to estimate the optimal output over time as a function of the environmental dynamics, (2) which networks are optimal for each level of environmental complexity, and (3) the equilibrium industry structure.

Vega-Redondo (1997) showed that imitation leads to the Walrasian outcome in Cournot Oligopoly. We generalize his result to aggregative quasi-submodular games. Examples are the Cournot Oligopoly, Bertrand games with differentiated complementary products, Common-Pool Resource games, Rent-Seeking games and generalized Nash-Demand games.

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Recent literature shows that learning in oligopoly games might in the long run result in the Cournot or in the Walras equilibrium. Which outcome is achieved seems to depend on the underlying learning dynamics. This paper analyzes the forces behind the learning mechanisms determining the long run outcome. Apart from the fact that there is a difference between social and individual learning, the key parameter is shown to be the degree of rationality of the learning agents: Learning the Cournot strategy requires the agents to acquire a large amount of information and to use sophisticated computational techniques, while the Walras strategy can be shown to be a particular ‘low rationality result‘.

I present a formal model of symmetric n-firm Cournot oligopoly with a heterogeneous population of profit optimizers and imitators...