Marco A. Janssen

Complex Responsive Processes (CRP) focus on the interaction between agents, where they exchange knowledge, opinions, experience, and values. In decentralized decision making, this could accelerate the monitoring, analysis, planning and execution process, as defined in a control mechanism like MAPE-K. For Multi-Agent Systems with a decentralized or hybrid architecture the gesture (e.g., agent expression) and response dynamics of complex responsive interaction could be valuable to reduce the entropy of a system. Until today, the CRP mechanisms have not been formalized in Multi-Agent decentralized decision making as it lacks a formal model to express inter-agent dialectics. This position paper discloses the area where an extension of the MAPE-K control cycle can be made to include the formalized CRP processes. This extension consists of a set of methods that include the responsive processes of multiple agents and will be used to update the Knowledge base in the MAPE-K model.

Chapter 21 focuses on controlled behavioural experiments, methods that randomly divide participants into different groups (treatments), controlling conditions across these treatments and allowing only the variable of interest to vary. Controlled behavioural experiments are used to test the conditions under which we can expect collective action to emerge. The chapter discusses controlled behavioural experiments in the form of common-pool resource games and public good games. It goes on to discuss the types of social-ecological systems (SES) problems and research questions commonly addressed by this set of methods, as well as their limitations, resource implications and new emerging research directions. The chapter also includes an in-depth case study showcasing the application of controlled behavioural experiments, and suggested further readings on these methods.

Even a simple human foraging system has a large number of moving parts. Foragers require a complex decision making process to effectively exploit the spatially and temporally variable resources in an environment. Here we present an agent-based modelling framework, based in optimal foraging theory, for agent foragers to make mobility and foraging decisions by weighing expected caloric returns against geographic and social factors, and forecasted future return rates. We apply our Paleoscape model to a spatially explicit South African coastal landscape to better understand the human foraging system of the Middle Stone Age, when foragers began systematically exploiting a wide variety of flora and fauna in both terrestrial and inter-tidal environments. We also discuss the broader importance of agent-based models of foraging systems for a wide variety of archaeological research questions.

Large scale collaboration is a fundamental characteristic of human society, and has recently manifested in the development and proliferation of online communities. These virtual social spaces provide an opportunity to explore large scale collaborations as natural experiments in which determinants of success can be tested. In order to do this, we first review previous work on meddling online communities to build an understanding of how these communities function. Having thus identified the operating mechanisms inherent in online communities, we propose a population ecology model of online communities that seeks to explain a number of statistical patterns from a selection of such communities.

Our understanding of human behavior is limited and consequently lacks a standard formal model of human behavior that could represent relevant behavior in social-ecological systems. In this paper we explore the consequences of alternative behavioral models using a simple dynamic system of agents of harvesting daisies in the well-known Daisyworld model. We explore the consequences of different behavioral assumptions and derive optimal tax policies that lead to sustainable outcomes for each of the theories.

The ability of groups to self-govern their common-pool resources is well documented (Ostrom, 1990). Whether common-pool resources are fish stocks or freshwater or forest products, the success of self-governance relates to the ability of appropriators to develop trust relationships, monitor and enforce agreements, and communicate among each other.

This chapter described the empirical calibration of a theoretical model based on data from field experiments. Field experiments on irrigation dilemmas were performed to understand how resource users overcome asymmetric collective action problems. The fundamental problem facing irrigation systems is how to solve two related collective action problems: (1) the provision of the physical and ecological infrastructure necessary to utilize the resource (water), and (2) the irrigation dilemma where the relative positions of “head-enders” and “tail-enders” generate a sequential access to the resource itself (water). If actors act as rational, self-interested, agents, it is difficult to understand how irrigation infrastructure would ever be constructed and maintained by the farmers obtaining water from a system as contrasted to a government irrigation bureaucracy. Wittfogel (1957) argued that a central control was indispensable for the functioning of larger irrigation systems and hypothesized that some state-level societies have emerged as a necessary side-effect of solving problems associated with the use of large-scale irrigation.

In this paper we apply the updated consumat approach to the case of diffusion of electric cars. We will discuss how data from a large sample can be used to parameterize a number of main behavioural drivers, and how these relate to behavioural processes. At this stage we explain how the data fit in the framework, and whereas a model is currently under development, first simulation results are to be available first during the ESSA conference.

Keywords: diffusion; electric cars; agent based modeling; human behavior; decision making; needs; consumat

In this paper, we present the results of an agent-based model of foraging of hominids. The model represents foraging activities in a landscape that is based on detailed measurements of food availability in the modern East African environments. These current landscapes are used as a model for the environment of the hominids one million years ago. We use the model to explore possible rankings of food preferences for different types of hominids (Homo ergaster and Australopithecus boisei) in different types of semi-arid landscapes. We let the agents adjust their preferences to maximize their calorie intake and show that A. boisei could not meet its calorie requirements in different landscapes.

Social-ecological systems are complex adaptive systems where social and biophysical agents are interacting at multiple temporal and spatial scales. The main challenge for the study of governance of social-ecological systems is improving our understanding of the conditions under which cooperative solutions are sustained, how social actors can make robust decisions in the face of uncertainty and how the topology of interactions between social and biophysical actors affect governance. We review the contributions of agent-based modeling to these challenges for theoretical studies, studies which combines models with laboratory experiments and applications of practical case studies. Empirical studies from laboratory experiments and field work have challenged the predictions of the conventional model of the selfish rational agent for common pool resources and public-good games. Agent-based models have been used to test alternative models of decision-making which are more in line with the empirical record. Those models include bounded rationality, other regarding preferences and heterogeneity among the attributes of agents. Uncertainty and incomplete knowledge are directly related to the study of governance of social-ecological systems. Agent-based models have been developed to explore the consequences of incomplete knowledge and to identify adaptive responses that limited the undesirable consequences of uncertainties. Finally, the studies on the topology of agent interactions mainly focus on land use change, in which models of decision-making are combined with geographical information systems. Conventional approaches in enviromental economics do not explicitly include non-convex dynamics of ecosystems, non-random interactions of agents, incomplete understanding, and empirically based models of behavior in collective action. Although agent-based modeling for social-ecological systems is in its infancy, it addresses the above features explicitly and is therefore potentially useful to address the current challenges in the study of governance of social-ecological systems.

The use of agent-based modeling (ABM) has recently been extended to the study of natural resource management and land-use and land-cover change. Many ABM applications have been at a conceptual and abstract level, which helps scholars to recognize how macro patterns can emerge from simple rules followed by agents at a micro-level. ABM has a greater potential than many other approaches to capturing the dynamic relationships between social and ecological systems. This paper contributes to a larger effort to explore how individual decision making by a heterogeneous set of landowners, given local biophysical conditions, led to the particular aggregate pattern of land-cover change in Indiana, with an emphasis on forest-cover change. In our preliminary effort, we created a model structure that allowed examination of the institutional impact of government programs on individual land-use decisions. Our model is based on the concept that an initial condition endows an agent with a particular set of beliefs and desires that could lead to any number of intentions, actions, and outcomes. Institutions have the potential to intervene in an agent’s decision-making process and alter their beliefs and desires by providing information and incentives. The next crucial step in our effort will be to extend this model to study the impact of other political institutions, such as taxation and zoning, as well as utilize the conceptual model to facilitate the implementation of institutions in the agent-based model.

Multi agent simulation (MAS) is a tool that can be used to explore the dynamics of different systems. Considering that many demographic phenomena have roots in individual choice behaviour and social interactions it is important that this behaviour is being translated in agent rules. Several behaviour theories are relevant in this context, and hence there is a necessity of using a meta-theory of behaviour as a framework for the development of agent rules. The consumat approach provides a basis for such a framework, as is demonstrated with a discussion of modelling the diffusion of contraceptives. These diffusion processes are strongly influenced by social processes and cognitive strategies. Different possible research lines are discussed which might be addressed with a multi-agent approach like the consumats.

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.

Addressing global change demands an integrative consideration of interactions between humans and the environment on a world wide scale. An assimilative integrated system approach seems to be appropriate for investigation of this complex global problem. In this paper an integrated modeling approach is proposed that is based on an evolutionary view on global change. A case study is worked out where images of the future using a multi-agent model are assessed, and where agents differ in their world view and thus also in their preferred management style. The perspective of agents may change due to new information they derive from the system. A simple model is constructed to illustrate the consequences of this approach on climate change scenarios.

Human activities change the environment on a global level. Global modelling is used to derive insights in the interactions between humans and their environment. However, the possibility to validate those global models is limited. In fact, too little information is available, many subjective assumptions are made and a single model cannot cover all relevant scale levels and processes. These limitations already appeared in the early seventies. Current global modelling activities still deal with the same dilemma’s, often in the same way as the strongly criticised world models of the early seventies. We sketch some recent developments which can help to manage the persistent dilemma’s. We focus on the use of different modelling paradigms and on the use of different world views to analyse the consequences of subjective assumptions to be made in global models.

Keywords: global modelling, validation, complexity, uncertainty.

The safe landing analysis has been devel­ oped to link short-term greenhouse gas emission targets to longer-term climate protection goals. The analysis was applied to the climate policy goals proposed by the European Union. This application and sev­eral presentations of the analysis during the negotiations on the Kyoto Protocol led to critical but constructive discussions. In this paper we discuss some of the key questions such as policy relevance, scien­tific credibility, use and adequacy of global indicators todetermine impact levels, tech­nological feasibility and economic aspects. The results from the safe landing analysis were generally accepted by the policy community because it bridges the gap between policy needs and the under­standing derived from complex but scienti­fically rigorous integrated assessment models.The selected indicators of the safe landing analysis are evaluated. It is shown that the indicators describing rates of change are as important for defining impacts and response policies as those describing only cumulative or absolute change. Lower levels of climatic change generally coincide with lower impact levels. However, only the lowest rates and levels of climate change allow natural eco­systems to adapt. It is further shown that the level of additional energy expenditures needed to meet such low impact levels strongly depends on the assumed techno­logical development rates.

When tackling a subject as complex as global change and sustainable development, it is essential to be able to frame the issues. This was one of the main reasons for developing the TARGETS model, an integrated model of the global system, consisting of metamodels of important subsystems. In this chapter, we introduce TARGETS. Building on the previous chapters, we elaborate on the possibilities and limitations of integrated assessment models. Some of the key issues discussed are aggregation, model calibration, and validation, and dealing with uncertainty.

This submodel simulates the supply and demand for fuels and electricity, given a certain level of economic activity. It is linked to other submodels, for example through investment flows, population sizes, and emissions. The energy model consists of five modules: Energy Demand, Electric Power Generation, and Solid, Liquid and Gaseous Fuel supply. Effects such as those of depletion, conservation, fuel substitution, technological innovation, and energy efficiency are incorporated in an integrated way, with prices as important signals. Renewable sources are included as a non-thermal electricity option and as commercial biofuels.

In this chapter we present simulation experiments and outcomes of the energy submodel TIME. First, the major controversies and uncertainties are discussed. Next, the cultural perspectives are introduced with reference to world energy, after which we clarify the way in which these are linked to assumptions and model routes. Some results of sensitivity and uncertainty analyses are also given. We discuss a few energy dystopias which could emerge if, for a given population-economy scenario, the world view and the management style within the energy system are discordant. Some conclusions are presented about the plausibility of and risks related to the Utopian energy futures. The impacts of the emissions from fossil fuel combustion on water, land, and element cycles are discussed in the next three chapters.

The objective of this paper is to demonstrate a methodology whereby reductions of greenhouse gas emissions can be allocated on a regional level with minimal deviation from the “business as usual emission scenario”. The methodology developed employs a two stage optimization process utilizing techniques of mathematical programming. The stage one process solves a world emission reduction problem producing an optimal emission reduction strategy for the world by maximizing an economic utility function. Stage two addresses a regional emission reduction allocation problem via the solution of an auxiliary optimization problem minimizing disruption from the above business as usual emission strategies. Our analysis demonstrates that optimal CO2 emission reduction strategies are very sensitive to the targets placed on CO2 concentrations, in every region of the world. It is hoped that the optimization analysis will help decision-makers narrow their debate to realistic environmental targets.