The Influence of Experimental and Computational Economics: Economics Back to the Future of Social Sciences

Economics has been a most puzzling science, namely since the neoclassical revolution defined the legitimate procedures for theorisation and quantification. Its epistemology has based on farce: decisive tests are not applied on dare predictions. As a consequence, estimation has finally been replaced by simulation, and empirical tests have been substituted by non-disciplined exercises of comparison of models with reality. Furthermore, the core concepts of economics defy the normally accepted semantics and tend to establish meanings of their own. One of the obvious instances is the notion of rationality, which has been generally equated with the apt use of formal logic or the ability to apply econometric estimation as a rule of thumb for daily life. In that sense, rationality is defined devoid of content, as alien to the construction of significance and reference by reason and social communication. The contradictory use of simulacra and automata, by John von Neumann and Herbert Simon, was a response to this escape of economic models from reality, suggesting that markets could be conceived of as complex institutions. But most mainstream economists did not understand or did not accept these novelties, and the empirical inquiry or the realistic representation of the action of agents and of their social interaction remained a minor domain of economics, and was essentially ignored by canonical theorizing. The argument of the current paper is based on a survey and discussion of the twin contributions of experimental and computational economics to these issues. Although mainly arising out of the mainstream, these emergent fields of economics generate challenging heuristics as well as new empirical results that defy orthodoxy. Their contributions both to the definition of the social meanings of rationality and to the definition of a new brand of inductive economics are discussed.

Coordination and allocation on land markets under increasing scale economies and heterogeneous actors - An experimental study

Economies of scale and scope are often not exploited in Western agriculture. A general reason is probably that various types of transaction costs limit coordination among farmers. A more specific explanation is that coordination on land markets or machinery cooperation is difficult to achieve when farmers are heterogeneous as some kind of price differentiation is necessary for a Pareto-superior solution. This paper investigates experimentally such a coordination game with heterogeneous agents using an example inspired by agricultural land markets. The experimental findings suggest that a Pareto-optimal solution may not be found when agents are heterogeneous. The findings provide evidence for market failures and cooperation deficits as reasons for unexploited economies of scale in agriculture. Our findings are consistent with coordination failures that appear to be driven by behavioural factors such as anchoring-and-adjustment, inequity aversion, and a reverse form of winner’s curse.Land Markets, Coordination and Allocation, Experimental Economics, Agricultural and Food Policy, Farm Management, Land Economics/Use,

Computational rationality and voluntary provision of public goods: an agent-based simulation model

The issue of the cooperation among private agents in realising collective goods has always raised problems concerning the basic nature of individual behaviour as well as the more traditional economic problems. The Computational Economics literature on public goods provision can be useful to study the possibility of cooperation under alternative sets of assumptions concerning the nature of individual rationality and the kind of interactions between individuals. In this work I will use an agent-based simulation model to study the evolution of cooperation among private agents taking part in a collective project: a high number of agents, characterised by computational rationality, defined as the capacity to calculate and evaluate their own immediate payoffs perfectly and without errors, interact to producing a public good. The results show that when the agents’ behaviour is not influenced either by expectations of others’ behaviour or by social and relational characteristics, they opt to contribute to the public good to an almost socially optimal extent, even where there is no big difference between the rates of return on the private and the public investment.Computational Economics; Agent-based models; Social Dilemmas; Collective Action; Public Goods

The role of information in multi-agent learning

This paper aims to contribute to the study of auction design within the domain of agent-based computational economics. In particular, we investigate the efficiency of different auction mechanisms in a bounded-rationality setting where heterogeneous artificial agents learn to compete for the supply of a homogeneous good. Two different auction mechanisms are compared: the uniform and the discriminatory pricing rules. Demand is considered constant and inelastic to price. Four learning algorithms representing different models of bounded rationality, are considered for modeling agents' learning capabilities. Results are analyzed according to two game-theoretic solution concepts, i.e., Nash equilibria and Pareto optima, and three performance metrics. Different computational experiments have been performed in different game settings, i.e., self-play and mixed-play competition with two, three and four market participants. This methodological approach permits to highlight properties which are invariant to the different market settings considered. The main economic result is that, irrespective of the learning model considered, the discriminatory pricing rule is a more e±cient market mechanism than the uniform one in the two and three players games, whereas identical outcomes are obtained in four players competitions. Important insights are also given for the use of multi-agent learning as a framework for market design.multi-agent learning; auction markets; design economics; agent-based computational economics

Approaches to the Security Analysis of Power Systems: Defence Strategies Against Malicious Threats

This report is intended to provide a conceptual framework for assessing the security risk to power systems assets and operations related to malicious attacks. The problem is analysed with reference to all the actors involved and the possible targets. The specific nature of the malicious attacks is discussed and representations in terms of strategic interaction are proposed. Models based on Game Theory and Multi Agent Systems techniques specifically developed for the representation of malicious attacks against power systems are presented and illustrated with reference to applications to small-scale test systems.JRC.G.6-Sensors, radar technologies and cybersecurit

Government Policy and the Probability of Coordination Failures

This paper introduces an approach to the study of optimal government policy in economies characterized by a coordination problem and multiple equilibria. Such models are often criticized as not being useful for policy analysis because they fail to assign a unique prediction to each possible policy choice. We employ a selection mechanism that assigns, ex ante, a probability to each equilibrium indicating how likely it is to obtain. We show how such a mechanism can be derived as the natural result of an adaptive learning process. This approach leads to a well-defined optimal policy problem, and has important implications for the conduct of government policy. We illustrate these implications using a simple model of technology adoption under network externalities.

Agent-Based Models and Human Subject Experiments

This paper considers the relationship between agent-based modeling and economic decision-making experiments with human subjects. Both approaches exploit controlled ``laboratory'' conditions as a means of isolating the sources of aggregate phenomena. Research findings from laboratory studies of human subject behavior have inspired studies using artificial agents in ``computational laboratories'' and vice versa. In certain cases, both methods have been used to examine the same phenomenon. The focus of this paper is on the empirical validity of agent-based modeling approaches in terms of explaining data from human subject experiments. We also point out synergies between the two methodologies that have been exploited as well as promising new possibilities.agent-based models, human subject experiments, zero- intelligence agents, learning, evolutionary algorithms

Using Multi-Agent Simulation to Explore the Contribution of Facilitation to GSS Transition

Significant prior research has shown that facilitation is a critical part of GSS transition. This study examines an under-researched aspect of facilitation—its contributions to self-sustained GSS use among group members. Integrating insights from Adaptive Structuration Theory, experimental economics, and the Collaboration Engineering literature, we formalize interactions of group members in GSS transition as strategic interactions in a minimum-effort coordination game. The contributions of facilitation are interpreted as coordination mechanisms to help group members achieve and maintain an agreement on GSS use by reducing uncertainties in the coordination game. We implement the conjectured coordination mechanisms in a multi-agent simulator. The simulator offers insights into the separate and combined effects of common facilitation practices during the lifecycle of GSS transition. These insights can help the Collaboration Engineering community to identify and package the facilitation routines that are critical for group members to achieve self-sustained GSS use and understand how facilitation routines should be adapted to different stages of GSS transition lifecycle. Moreover, they indicate the value of the multi-agent approach in uncovering new insights and representing the issue of GSS transition with a new view

The influence of experimental and computational economics: Economics back to the future of social sciences

Economics has been a most puzzling science, namely since the neoclassical revolution defined the legitimate procedures for theorisation and quantification. Its epistemology has based on farce: decisive tests are not applied on dare predictions. As a consequence, estimation has finally been replaced by simulation, and empirical tests have been substituted by non-disciplined exercises of comparison of models with reality. Furthermore, the core concepts of economics defy the normally accepted semantics and tend to establish meanings of their own. One of the obvious instances is the notion of rationality, which has been generally equated with the apt use of formal logic or the ability to apply econometric estimation as a rule of thumb for daily life. In that sense, rationality is defined devoid of content, as alien to the construction of significance and reference by reason and social communication. The contradictory use of simulacra and automata, by John von Neumann and Herbert Simon, was a response to this escape of economic models from reality, suggesting that markets could be conceived of as complex institutions. But most mainstream economists did not understand or did not accept these novelties, and the empirical inquiry or the realistic representation of the action of agents and of their social interaction remained a minor domain of economics, and was essentially ignored by canonical theorizing. The argument of the current paper is based on a survey and discussion of the twin contributions of experimental and computational economics to these issues. Although mainly arising out of the mainstream, these emergent fields of economics generate challenging heuristics as well as new empirical results that defy orthodoxy. Their contributions both to the definition of the social meanings of rationality and to the definition of a new brand of inductive economics are discussed