Journal of Economic Interaction and Coordination

We study the relationship between communication network topologies, namely the small-world networks introduced by Watts and Strogatz, and the simulation results of an artificial stock market, here the Frankfurt Artificial Stock Market. Heterogeneous interacting agents communicate their success and trading strategy to their nearest neighbors. A process of information diffusion arises through the adaptive behavior of agents when encountering more successful strategies in their direct neighborhood. We will show that an increasing rewiring probability of the small-world network will lead to higher volatility and distortion within our simulation model. It seems probable that the spatial position of traders within a communication network affects the price building process.

This paper provides a survey of the development and contributions of agent-based modeling to economic analysis, without any claim to be exhaustive. Our perspective is particularly shaped by the papers presented at the 17th Wehia conference, a selection of which are published in this special issue. The paper shows how agent-based models have developed, and how they have improved our understanding not only of macroeconomic disequilibria but also of the possibilities of the emergence of equilibrium in such realistic systems. It also reviews the progress made in our understanding of real markets, and lists some of the improvements needed to establish these models as alternative tools to the present orthodox models.

We develop a dynamic model of information transmission and aggregation in social networks in which continued membership in the network is contingent on the accuracy of opinions. Agents have opinions about a state of the world and form links to others in a directed fashion probabilistically. Agents update their opinions by averaging those of their connections, weighted by how long their connections have been in the system. Agents survive or die based on how far their opinions are from the true state. In contrast to the results in the extant literature on DeGroot learning, we show through simulations that for some parameterizations the model cycles stochastically between periods of high connectivity, in which agents arrive at a consensus opinion close to the state, and periods of low connectivity, in which agents’ opinions are widely dispersed.

A reliable method of options pricing in real time would help various players, including hedgers and speculators, to make informed decisions. In this study, we develop an extensive simulation with multiple business environments, which includes the use of real data from the S&P 500 Index between the years 2010–2017 for the 30 days prior to expiration of the options. Forecasted tradability is computed based on the SH model: a theoretical model of real-time options pricing that takes into account players’ heterogeneity with regard to their willingness to accept offers proposed by the opposing player. The quality of the model is examined for the scenario in which the model players are speculators who act against the real market prices. We show that the equilibrium prices predicted by the SH model are close to the market prices (a deviation of up to approx. 3%) in an In-The-Money environment. Additionally, the tougher the players (i.e., the greater their level of unwillingness to accept a bid from the opposing player), the higher the average tradability. We also find that the level of willingness of the players has a greater effect on tradability than does option moneyness or the market trend.

We compare price dynamics of different market protocols (batch auction, continuous double auction and dealership) in an agent-based artificial exchange. In order to distinguish the effects of market architectures alone, we use a controlled environment where allocative and informational issues are neglected and agents do not optimize or learn. Hence, we rule out the possibility that the behavior of traders drives the price dynamics. Aiming to compare price stability and execution quality in broad sense, we analyze standard deviation, excess kurtosis, tail exponent of returns, volume, perceived gain by traders and bid-ask spread. Overall, a dealership market appears to be the best candidate, generating low volume and volatility, virtually no excess kurtosis and high perceived gain.

To pass from a deterministic dynamics of aggregate quantities to a probabilistic dynamics of a system of microvariables that describe the individual strategies of a population of economic agents, the route is that of Boltzmann’s kinetic theory at the half of XIX century (more suitable than that of Gibbs’ statistical mechanics), that is the introduction of n “elements” (molecules, agents,…), submitted to some microdynamics, wherefrom to derive the macroscopic behavior. The macrovariate is interpreted as a (time) mean of the average (on all elements) of the individual study-property at time t. The micro-derivation looks unproblematic if means and averages tend to constant values in the limit n → ∞. If this property, defined “self-averaging” in some recent papers by Aoki, holds, it would separate a deterministic result from fluctuations; consequently well defined macroeconomic deterministic relations prevail. However it is easy to show that in most cases in economy this property does not hold, due to long-range correlation existing among economic agents. If individual agents are not independent but exchangeable, also in the limit n → ∞ the coefficient of variation of the macrovariable is finite, which tends to a random limit rather than a constant. Finally the term “indistinguishable agent” is criticized, and the alternative “exchangeable agent” is discussed.

Strong reciprocity is a fundamental human characteristic associated with our extraordinary sociality and cooperation. Laboratory experiments on social dilemma games and many field studies have quantified well-defined levels of cooperation and propensity to punish/reward. The level of cooperation is observed to be strongly dependent on the availability of punishments and/or rewards. Here, we propose an operational approach based on the evolutionary selection of prosocial behaviors to explain the quantitative level of the propensity to punish in three experimental set-ups. A simple cost/benefit analysis at the level of a single agent, who anticipates the action of her fellows, determines an optimal level of altruistic punishment, which explains quantitatively experimental results on a third-party punishment game, the ultimatum game and an altruistic punishment game. We also report numerical simulations of an evolutionary agent-based model of repeated agent interactions with feedback by punishments, which confirms that the propensity to punish is a robust emergent property selected by the evolutionary rules of the model. The cost-benefit reasoning is not to be taken literally but rather to embody the result of the selection pressure of co-evolving agents that have make them converge to their preferences (which can be seen as either hard-wired and/or culturally selected). In this view, the prosocial preference of humans is a collective emergent process, robustly selected by adaptation and selection. Our main contribution is to use evolutionary feedback selection to quantify the value of the prosocial propensity to punish, and test this prediction on three different experimental set-ups.

This paper analyzes the impact of inequality in the distribution of endowments on cooperation. We conduct a lab experiment using a dynamic Public Good Game to test this relation. We introduce the possibility of choosing among three different redistribution rules: Equidistribution, Proportional to contribution and Progressive to endowment. This novelty in a dynamic environment allows us to analyze how the inequality within groups changes according to individual choices and to investigate if players show inequity averse preferences. Results show that inequality has a negative impact on individual contribution. Players act in order to reduce the initial exogenous inequality. Indeed, in the Treatment with the highest level of inequality, agents vote for reducing the endowment heterogeneity. Moreover, individual contribution is strongly influenced by others’ contributions.

How will the novel coronavirus evolve? I study a simple epidemiological model, in which mutations may change the properties of the virus and its associated disease stochastically and antigenic drifts allow new variants to partially evade immunity. I show analytically that variants with higher infectiousness, longer disease duration, and shorter latent period prove to be fitter. “Smart” containment policies targeting symptomatic individuals may redirect the evolution of the virus, as they give an edge to variants with a longer incubation period and a higher share of asymptomatic infections. Reduced mortality, on the other hand, does not per se prove to be an evolutionary advantage. I then implement this model as an agent-based simulation model in order to explore its aggregate dynamics. Monte Carlo simulations show that a) containment policy design has an impact on both speed and direction of viral evolution, b) the virus may circulate in the population indefinitely, provided that containment efforts are too relaxed and the propensity of the virus to escape immunity is high enough, and crucially c) that it may not be possible to distinguish between a slowly and a rapidly evolving virus by looking only at short-term epidemiological outcomes. Thus, what looks like a successful mitigation strategy in the short run, may prove to have devastating long-run effects. These results suggest that optimal containment policy must take the propensity of the virus to mutate and escape immunity into account, strengthening the case for genetic and antigenic surveillance even in the early stages of an epidemic.