Learning to Be Thoughtless: Social Norms and Individual Computation

This paper extends the literature on the evolution of norms with anagent-based modelcapturing a phenomenon that has been essentially ignored, namely thatindividual thought – orcomputing – is often inversely related to the strength of a social norm.Once a norm isentrenched, we conform thoughtlessly. In this model, agents learn how tobehave (what normto adopt), but – under a strategy I term Best Reply to Adaptive SampleEvidence – they also learnhow much to think about how to behave. How much they are thinking affects howthey behave,which – given how others behave – affects how much they think. Inshort, there is feedbackbetween the social (inter-agent) and internal (intra-agent) dynamics. Inaddition, we generate thestylized facts regarding the spatio-temporal evolution of norms: localconformity, global diversity,and punctuated equilibria.     

Bilateral Trade and ‘Small-World’ Networks

Trade requires search, negotiation, and exchange, which are activities thatabsorb resources. Thispaper investigates how different trade networks attend to these activities.An artificial marketis constructed in which autonomous agents endowed with a stock of goods seekout partners,negotiate a price, and then trade with the agent offering the best deal.Different trade networksare imposed on the system by restricting the set of individuals with whom anagent cancommunicate. We then compare the path to the eventual equilibrium as well asthe equilibriumcharacteristics of each trade network to see how each system dealt with thetasks of search,negotiation, and exchange.Initially, all agents are free to trade with any individual in the globalmarket. In such a world,global resources are optimally allocated with few trades, but only after atremendous amount ofsearch and negotiation. If trade is restricted within disjoint localboundaries, search is simple butglobal efficiency elusive. However, a hybrid model in which most agents tradelocally but a fewagents trade globally results in an economy that quickly reaches a Paretooptimal equilibriumwith significantly lower search and negotiation costs. Such small-worldnetworks occur innature and may help explain the ease with which most of us acquire goods fromaround theworld. We also show that there are private incentives for such a system toarise.     

Coordination in networks: An economic equilibrium analysis

Networks utilizing modern communication technologies can offer competitive advantages to those using them wisely. But due to the existence of network effects, planning and operating cooperation networks is difficult. In this paper, the strategic situation of individual agents deciding on network participation is analysed. A systematic equilibrium analysis using computer-based simulations reveals principal solution scenarios for network agents and shows that network cooperation problems might frequently be not as difficult to resolve as often feared. In particular, strategic situations are identified showing that a majority of individuals might be better off getting the optimal solution from a central principal and that in many cases simple information intermediation can solve the start-up problem notorious in network economics.     

LEARNING OLIGOPOLISTIC COMPETITION IN ELECTRICITY AUCTIONS

This paper addresses the problem of auction markets efficiency within the context of recently liberalized electricity markets. Two different auction mechanisms, i.e., the uniform and the discriminatory price setting rules, have been employed worldwide in designing electricity markets. In this paper, we study the relative efficiency of the two auction mechanisms in the framework of the learning-in-games approach. The behavior of electricity suppliers are modeled by means of an adaptive learning algorithm and the demand is assumed to be constant and inelastic, according to a common hypothesis in electricity market modeling. Computational experiments results are interpreted according game theoretical solutions, i.e., Nash equilibria and Pareto optima. Different economic scenarios corresponding to a duopoly and a tripoly competition with different level of demand are considered. Results show that in the proposed conditions, sellers learn to play competitive strategies, which correspond to Nash equilibria. Finally, this study establishes that, in the presented computational setting and economic scenarios, the discriminatory auction mechanism results more efficient than the uniform auction one.     

FROM DISCRETE-TIME MODELS TO CONTINUOUS-TIME, ASYNCHRONOUS MODELING OF FINANCIAL MARKETS

Most agent-based simulation models of financial markets are discrete-time in nature. In this paper, we investigate to what degree such models are extensible to continuous-time, asynchronous modeling of financial markets. We study the behavior of a learning market maker in a market with information asymmetry, and investigate the difference caused in the market dynamics between the discrete-time simulation and continuous-time, asynchronous simulation. We show that the characteristics of the market prices are different in the two cases, and observe that additional information is being revealed in the continuous-time, asynchronous models, which can be acted upon by the agents in such models. Because most financial markets are continuous and asynchronous in nature, our results indicate that explicit consideration of this fundamental characteristic of financial markets cannot be ignored in their agent-based modeling.     

An Evolutionary Model of Endogenous Business Cycles

In this paper, we present an evolutionary model of industry dynamics yielding endogenous business cycles with ‘Keynesian’ features. The model describes an economy composed of firms and consumers/workers. Firms belong to two industries. The first one performs R&D and produces heterogeneous machine tools. Firms in the second industry invest in new machines and produce a homogenous consumption good. Consumers sell their labor and fully consume their income. In line with the empirical literature on investment patterns, we assume that the investment decisions by firms are lumpy and constrained by their financial structures. Moreover, drawing from behavioral theories of the firm, we assume boundedly rational expectation formation. Simulation results show that the model is able to deliver self-sustaining patterns of growth characterized by the presence of endogenous business cycles. The model can also replicate the most important stylized facts concerning micro- and macro-economic dynamics. Indeed, we find that investment is more volatile than GDP; consumption is less volatile than GDP; investment, consumption and change in stocks are procyclical and coincident variables; employment is procyclical; unemployment rate is anticyclical; firm size distributions are skewed but depart from log-normality; firm growth distributions are tent-shaped. JEL Classifications: C15, C22, C49, E17, E22, E32.     

What is Computational Economics?

Computational Economics is rapidly evolving into an independent branche in economics. This paper describes the current and future developments within the field of Computational Economics.     

Computational Economics: Help for the Underestimated Undergraduate

Our concern in this paper is that the capability of economics undergraduates is substantially underestimated in the design of the present college curriculum and that our students are insufficiently challenged and motivated. Students enter our classrooms with substantial previous knowledge about computers and computation and we are not taking full advantage of this opportunity. We suggest a set of examples from computational economics which are challenging enough to motivate students and simple enough that they can master them within a few hours. By encouraging the students to modify the models in directions of their own interest, avenues for creative endeavor are opened which deeply involve the students in their own education.     

Modeling the expectations of inflation in the OLG model with genetic programming

 In this paper, genetic programming (GP) is employed to model learning and adaptation in the overlapping generations model, one of the most popular dynamic economic models. Using a model of inflation with multiple equilibria as an illustrative example, we show that our GP-based agents are able to coordinate their actions to achieve the Pareto-superior equilibrium (the low-inflation steady state) rather than the Pareto inferior equilibrium (the high-inflation steady state). We also test the robustness of this result with different initial conditions, economic parameters, GP control parameters, and the selection mechanism. We find that as long as the survival-of-the-fittest principle is maintained, the evolutionary operators are only secondarily important. However, once the survival-of-the-fittest principle is absent, the well-coordinated economy is also gone and the inflation rate can jump quite wildly. To some extent, these results shed light on the biological foundations of economics.