Learning in evolutionary environments

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Learning in Evolutionary Environments

The purpose of this work is to present a sort of short selective guide to an enormous and diverse literature on learning processes in economics. We argue that learning is an ubiquitous characteristic of most economic and social systems but it acquires even greater importance in explicitly evolutionary environments where: a) heterogeneous agents systematically display various forms of "bounded rationality"; b) there is a persistent appearance of novelties, both as exogenous shocks and as the result of technological, behavioural and organisational innovations by the agents themselves; c) markets (and other interaction arrangements) perform as selection mechanisms; d) aggregate regularities are primarily emergent properties stemming from out-of-equilibrium interactions. We present, by means of examples, the most important classes of learning models, trying to show their links and differences, and setting them against a sort of ideal framework of "what one would like to understand about learning...". We put a signifiphasis on learning models in their bare-bone formal structure, but we also refer to the (generally richer) non-formal theorising about the same objects. This allows us to provide an easier mapping of a wide and largely unexplored research agenda.Learning, Evolutionary Environments, Economic Theory, Rationality

Detecting change and dealing with uncertainty in imperfect evolutionary environments

Imperfection of information is a part of our daily life; however, it is usually ignored in learning based on evolutionary approaches. In this paper we develop an Imperfect Evolutionary System that provides an uncertain and chaotic imperfect environment that presents new challenges to its habitants. We then propose an intelligent methodology which is capable of learning in such environments. Detecting changes and adapting to the new environment is crucial to exploring the search space and exploiting any new opportunities that may arise. To deal with these uncertain and challenging environments, we propose a novel change detection strategy based on a Particle Swarm Optimization system which is hybridized with an Artificial Neural Network. This approach maintains a balance between exploitation and exploration during the search process. A comparison of approaches using different Particle Swarm Optimization algorithms show that the ability of our learning approach to detect changes and adapt as per the new demands of the environment is high

Interpreting Economic Change: Evolution, Structures and Games

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A distributed evolutionary algorithm with a superlinear speedup for solving the vehicle routing problem

In this paper we present a distributed evolutionary algorithm for solving the capacitated vehicle routing problem. Our algorithm consists of autonomous processes that create heterogeneous evolutionary environments, perform evolution on separate populations of chromosomes, and communicate asynchronously through occasional migrations of chromosomes. The paper also presents experiments where the algorithm has been tested on some benchmark problem instances. By measuring the effects of distribution on solution quality and on computing time, the experiments confirm that the algorithm achieves a superlinear speedup

‘Ethical Novelty’: new insights into economic change

Agents’ knowledge combines their perception of what reality is with their conception of what reality should be. “Ethical dynamics” refers to the evolution in the latter conception. This is a key element to explain changes in agents’ objectives of action, which usually do not result simply from interaction or “cognitive dynamics”. “Ethical novelties” are important sources of economic change. They consist of changes in the structure of action objectives which result from ethical dynamics.knowledge; action plan; ethical novelty; cognitive and ethical dynamics; economic change

The Abduction of Disorder in Psychiatry

The evolutionary cornerstone of J. C. Wakefield's (1999) harmful dysfunction thesis is a faulty assumption of comparability between mental and biological processes that overlooks the unique plasticity and openness of the brain?s functioning design. This omission leads Wakefield to an idealized concept of natural mental functions, illusory interpretations of mental disorders as harmful dysfunctions, and exaggerated claims for the validity of his explanatory and stipulative proposals. The authors argue that there are numerous ways in which evolutionarily intact mental and psychological processes, combined with striking discontinuities within and between evolutionary and contemporary social/cultural environments, may cause non-dysfunction variants of many widely accepted major mental disorders. These examples undermine many of Wakefield's arguments for adopting a harmful dysfunction concept of mental disorder