Behaviour in commons dilemmas: Homo economicus and Homo psychologicus in an ecological-economic model

Abstract

In mainstream economy, behaviour is often formalised following the rational actor-approach. However, in real life the behaviour of people is typified by multidimensional optimisation. To realise this, people engage in cognitive processes such as social comparison, imitation and repetitive behaviour (habits) so as to efficiently use their limited cognitive resources. A multi-agent simulation program is being developed to study how such micro-level processes affect macro-level outcomes. Sixteen agents are placed in a micro-world, consisting of a lake and a gold mine. Each agent's task is to satisfy its personal needs by fishing and/or mining, whereby they find themselves in a commons dilemma facing the risk of resource depletion. Homo economicus and Homo psychologicus are formalised to study the effects of different cognitive processes on the agents' behaviour. Results show that for the H. psychologicus the transition from a fishing to a mining society is more complete than for the H. economicus. Moreover, introducing diversity in agents' abilities causes the H. economicus on the average to decrease its time spent working, whereas for the H. psychologicus we observe an increase in the time spent working. These results confirm that macro-level indicators of sustainability, such as pollution and fish-harvest, are strongly and predictably affected by behavioural processes at the micro-level. It is concluded that the incorporation of a micro-level perspective on human behaviour within integrated models of the environment yields a better understanding and eventual management of the processes involved in environmental degradation.

Introduction

To improve our understanding of the world, scientists usually try to capture important aspects of a real-world system in a model. Scientific models allow for experimentation with different assumptions regarding how the real-world system works, thereby improving our understanding of the processes that govern the behaviour of a real-world system. Scientific models can, in a broad sense, be considered as more or less formalised mental maps (Rosen, 1987, Rosen, 1991). They play a crucial role in research and their development often proceeds through isomorphisms, that is, by hypothesising that two different real-world systems share the same formal description. This modelling-by-analogy has played an important role in economic science in an attempt to describe complex economic (sub)system(s). For instance, there are clear isomorphisms between neo-classical equilibrium theory and the laws of chemical equilibrium as formulated by, e.g., Guldberg and Waage (1867), and between theory on the production of goods and classical thermodynamics. In the physical sciences, the rather black-box macro-level description of physical systems has been complemented with micro-level descriptions in which system elements are identified as separate entities having certain characteristics such as mass and velocity. In ecological and economic systems the elements have many more degrees of freedom and a much larger variety in their interactions — which makes them complex systems — and hence a modelling tool must be applied that is capable of dealing with this complexity whilst yielding comprehensible outcomes. Multi-agent simulation provides a tool that is suitable for studying how micro-level characteristics affect macro-level system behaviour.

The growth in computer processing speed was one of the important conditions for the rapid growth of multi-agent simulations to study behavioural dynamics in social settings. Overviews of this developing area can be found in Vallacher and Nowak, 1994, Doran and Gilbert, 1994, Gilbert and Conte, 1995, Conte et al., 1997, Liebrand et al., 1998 and Gilbert and Troitzsch (1999). Especially within the paradigm of game theory many simulations have been developed that involved the management of a common resource. However, in these simulations the agent rules are usually aimed at maximising outcomes instead of representing real human behaviour.

In the multi-agent approach applied in the work presented here, agents are equipped with explicit behavioural strategies that depend on information and expectations about the environment — including other agents. In this way more behavioural richness can be incorporated in the simulations than has been done so far. This allows including a larger part of valid insights from social science research about human behaviour. Thus the multi-agent model gives a more intricate understanding of the system and of the ways in which it could be influenced. The disadvantages mirror these advantages: multi-agent models are hard to validate empirically, and they often have to rely on anecdotal evidence for their credibility. The reason is that it is difficult to find unequivocal empirical evidence for the very micro-level laws that give the models their richness. It may well be that this 'weakness' of our empirical knowledge is an inherent feature of complex systems (Janssen and De Vries, 1999). An interesting point is to see whether these simulations give rise to certain macro-properties ('emergent properties') which link them to the classical macro-approaches in social and economic science.

In this paper we demonstrate a multi-agent modelling approach aimed at introducing psychological rules that guide the behaviour of artificial agents. These artificial agents, which we have named ‘consumats’, are being tested in the ecological-economic model called ‘Lakeland’. Lakeland includes a renewable fish stock and a gold mine as need-satisfying behavioural opportunities. Before we demonstrate the consumats’ behaviour in Lakeland, we will first discuss a social-scientific perspective on resource management and the common dilemma paradigm. Following that, we will dedicate a separate section to the consumat model.