Marco A. Janssen

An approach is introduced to combine survey data with multi-agent simulation models of consumer behaviour to study the diffusion process of organic food consumption. This methodology is based on rough set theory, which is able to translate survey data into behavioural rules. The topic of rule induction has been extensively investigated in other fields and in particular in learning machine, where several efficient algorithms have been proposed. However, the peculiarity of the rough set approach is that the inconsistencies in a data set about consumer behaviour are not aggregated or corrected since lower and upper approximation are computed. Thus, we expect that rough sets theory is suitable to extract knowledge in the form of rules within a consistent theoretical framework of consumer behaviour.

We report on a series of computer simulation experiments regarding the management of a common resource. We were particularly interested in the effects of uncertainty and satisfaction on the harvesting behaviour of simulated agents. The experimental study of long-term dynamics of threatened resources can hardly be carried out using human subjects. We therefore experimented with simulated consumers, the so-called consumats, whose properties are derived from a comprehensive, multitheoretical model of consumer behaviour. A consumat is equipped with needs and abilities, and may engage in different cognitive processes, such as deliberation, social comparison, imitation, and repetition of previous behaviour. In a first simulation experiment we show as to how uncertainty may stimulate an imitation effect that promotes over-harvesting. In two subsequent series of experiments, we show that increased uncertainty results in an increased ‘optimism’ of consumats regarding future outcomes, an increased likelihood of imitative behaviour, and a lesser adaptation of harvesting behaviour during resource depletion. These ‘process-effects’ promote higher levels of harvesting from a collective resource. The main experimental conclusions and the issue of validating simulation results are discussed.

In this article, we identify four typical roles played by computer models in environmental policy-making, and explore the relationship of these roles to different stages of policy development over time. The four different roles are: models as eye-openers, models as arguments in dissent, models as vehicles in creating consensus and models for management. A general environmental policy life cycle is used to assess the different roles models play in the policy process. The relationship between the roles of models and the different stages of the policy life cycle is explored with a selection of published accounts of computer models and their use in environmental policy-making.

Keywords: Computer models; Environmental policy; Policy life cycle; Policy process

Approaches to natural resource management are often based on a presumed ability to predict probabilistic responses to management and external drivers such as climate. They also tend to assume that the manager is outside the system being managed. However, where the objectives include long-term sustainability, linked social-ecological systems (SESs) behave as complex adaptive systems, with the managers as integral components of the system. Moreover, uncertainties are large and it may be difficult to reduce them as fast as the system changes. Sustainability involves maintaining the functionality of a system when it is perturbed, or maintaining the elements needed to renew or reorganize if a large perturbation radically alters structure and function. The ability to do this is termed “resilience.” This paper presents an evolving approach to analyzing resilience in SESs, as a basis for managing resilience. We propose a framework with four steps, involving close involvement of SES stakeholders. It begins with a stakeholder-led development of a conceptual model of the system, including its historical profile (how it got to be what it is) and preliminary assessments of the drivers of the supply of key ecosystem goods and services. Step 2 deals with identifying the range of unpredictable and uncontrollable drivers, stakeholder visions for the future, and contrasting possible future policies, weaving these three factors into a limited set of future scenarios. Step 3 uses the outputs from steps 1 and 2 to explore the SES for resilience in an iterative way. It generally includes the development of simple models of the system’s dynamics for exploring attributes that affect resilience. Step 4 is a stakeholder evaluation of the process and outcomes in terms of policy and management implications. This approach to resilience analysis is illustrated using two stylized examples.

We analyse commercially operated rangelands as coupled systems of people and nature. The biophysical components include: (i) the reduction and recovery of potential primary production, re ected as changes in grass production per unit of rainfall; (ii) changes in woody plants dependent on the grazing and re regimes; and (iii) livestock and wool dynamics in uenced by season, condition of the rangeland and numbers of wild and feral animals. The social components include the managers, who vary with regard to a range of cognitive abilities and lifestyle choices, and the regulators who vary in regard to policy goals.

We compare agent-based and optimization models of a rangeland system. The agent-based model leads to recognition that policies select for certain management practices by creating a template that governs the trajectories of the behaviour of individuals, learning, and overall system dynamics. Conservative regu- lations reduce short-term loss in production but also restrict learning. A free-market environment leads to severe degradation but the surviving pastoralists perform well under subsequent variable conditions. The challenge for policy makers is to balance the needs for learning and for preventing excessive degra- dation. A genetic algorithm model optimizing for net discounted income and based on a population of management solutions (stocking rate, how much to suppress re, etc.) indicates that robust solutions lead to a loss of about 40% compared with solutions where the sequence of rainfall was known in advance: this is a similar gure to that obtained from the agent-based model.

We conclude that, on the basis of Levin’s three criteria, rangelands with their livestock and human managers do constitute complex adaptive systems. If this is so, then command-and-control approaches to rangeland policy and management are bound to fail.

Keywords: rangelands; complex adaptive system; resilience; institutions; agent-based models

Environmental processes have been modelled for decades. However, the need for integrated assessment and modeling (IAM) has grown as the extent and severity of environmental problems in the 21st Century worsens. The scale of IAM is not restricted to the global level as in climate change models, but includes local and regional models of environmental problems. This paper discusses various definitions of IAM and identifies five different types of integration that are needed for the effective solution of environmental problems. The future is then depicted in the form of two brief scenarios: one optimistic and one pessimistic. The current state of IAM is then briefly reviewed. The issues of complexity and validation in IAM are recognised as more complex than in traditional disciplinary approaches. Communication is identified as a central issue both internally among team members and externally with decision-makers, stakeholders and other scientists. Finally it is concluded that the process of integrated assessment and modelling is considered as important as the product for any particular project. By learning to work together and recognise the contribution of all team members and participants, it is believed that we will have a strong scientific and social basis to address the environmental problems of the 21st Century.

Keywords: Integrated assessment and modelling; Integration; Communication; Multi-disciplinary teams

This paper discusses the evolution of rules between people for the management of ecosystems. Four aspects of the rules are discussed: coding, creation, selection, and memory. The immune system provides us a useful metaphor to relate these four aspects into a coherent framework. We sketch a framework for a computational model to study the evolution of rules for the management of ecosystems.