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on Evolutionary Economics |
| By: | Sylvie Geisendorf (Department of Economics, University of Kassel) |
| Abstract: | Genetic Algorithms (GA) have been used for some years now to depict learning in economic models. Some authors criticize their use on the basis that they are a biologically motivated procedure having nothing to do with human learning. One argument of this paper is that the criticism of GA is focused at the wrong point – and was probably incurred by the much too simple applications we have seen so far. It is not primarily the origin of a model we should be concerned of, but its general characteristics and the specification in its current use. After a brief introduction into the procedure the paper tries to show why GA offer some important features for the modelling of bounded rationality. Learning models based on them are among the few that create novelty and describe the mechanisms of selection, recombination and variation by which novelty is generated. The paper discusses the criticism of GA and argues that the biological origin of the model should not be a substantial problem. The biological features of the model are only a shell in which the general mechanisms of evolutionary processes are imbedded. An up to now underestimated problem however lies in the adequate specification of the fitness function of GA. Proponents as well as critics of GA seemed to have overlooked the necessary distinction between internal fitness criteria of the agents and external criteria of the economy. Both are relevant for economic selection processes and have their proper place in the model. If this is respected GA based learning models can be a useful tool to investigate economic evolution. |
| Keywords: | Evolutionary Economics, Genetic Algorithms, Learning, Bounded Rationality, Modelling, Methodological work |
| Date: | 2007–12 |
| URL: | http://d.repec.org/n?u=RePEc:kas:poabec:2007-5&r=evo |
| By: | Javier Rivas |
| Abstract: | We investigate learning in a setting where each period a population has to choose between two actions and the payoff of each action is unknown by the players. The population learns according to reinforcement and the environment is non-stationary, meaning that there is correlation between the payoff of each action today and the payoff of each action in the past. We show that when players observe realized and foregone payoffs, a suboptimal mixed strategy is selected. On the other hand, when players only observe realized payoffs, a unique action, which is optimal if actions perform different enough, is selected in the long run. When looking for efficient reinforcement learning rules, we find that it is optimal to disregard the information from foregone payoffs and to learn as if only realized payoffs were observed. |
| Keywords: | Adaptive Learning, Markov Chains, Non-stationarity, Reinforcement Learning |
| JEL: | C73 |
| Date: | 2008 |
| URL: | http://d.repec.org/n?u=RePEc:eui:euiwps:eco2008/13&r=evo |
| By: | Uschi Backes-Gellner (Institute for Strategy and Business Economics, University of Zurich); Donata Bessey (Institute for Strategy and Business Economics, University of Zurich); Kerstin Pull (Eberhard Karls Universitaet Tuebingen); Simone Tuor (Institute for Strategy and Business Economics, University of Zurich) |
| Abstract: | In this survey article, we review results from behavioural and experimental economics that have a potential application in the field of personnel economics. While personnel economics started out with a “clean” economic perspective on human resource management (HRM), recently it has broadened its perspective by increasingly taking into account the results from laboratory experiments. Besides having inspired theory-building, the integration of behavioural economics into personnel economics has gone hand in hand with a strengthening of empirical analyses (field experiments and survey data) complementing the findings from the laboratory. Concentrating on employee compensation as one particular field of application, we show that for personnel economics there is indeed much to be learnt from the recent developments in behavioural economics. Moreover, integrating behavioural economics into personnel economics bears the chance of eventually reconciling personnel economics and “classic” HRM analysis that has a long tradition of relying on social psychology as a classical point of reference. |
| Keywords: | Behavioural Economics, Personnel Economics |
| JEL: | J3 M52 C9 |
| Date: | 2008–02 |
| URL: | http://d.repec.org/n?u=RePEc:iso:wpaper:0077&r=evo |