Modelling short term equilibrium and long term change in a natural way

Why would an agent produce and supply something if it got nothing in return? In order to investigate how complex systems, such as biological and economic systems, organize themselves, McLeod (2015) constructs a simple economic model for a biological system. In the context of a dimension model, it was shown that if exchange of resources between creatures is based on relative scarcity, we get a similar outcome to that produced by a market economy, even though such exchanges are not reciprocal. Specifically the ‘biological economy’ constructed in McLeod (2015) promotes the development of specialization and interdependence, and the number of creatures increases over time. These may be construed as large scale, or system, trends. The work presented in this thesis extends McLeod (2015). It develops a multi–sector general equilibrium model of an economy in which resource-based processes are modelled, in order to understand evolution from an economic perspective. The model is based on habitual behaviour represented by Markov chains. It applies particularly, but not exclusively, to biological systems and to pre–market human economies. Interestingly, the interplay between producers of scarce resources and consumers of those resources generates various kinds of agent number and system trajectories. These range from expanding to collapsing and oscillating to stable, depending on the ‘efficiency’ of the agents. Such dynamics occur even though we do not assume any explicit law of motion, objective function, or maximisation principle. The model demonstrates that: (̧‘–) mutation/learning will cause a progressive increase in the specialization, interdependence and size of the economy; and (̧‘–̧‘–) a path dependent outcome is possible. Overall, the work contributes to our economic understanding of systems by grounding the dynamics of those systems in the cut and thrust of evolutionary competition, rather than in the more aloof view of agent behaviour suggested by abstract optimization economics.