Identifying robust response options to manage environmental change using an ecosystem approach:a stress-testing case study for the UK

A diverse range of response options was evaluated in terms of their utility for sustaining ecosystem services in the UK. Robustness of response options was investigated by applying a ‘stress-testing’ method which evaluated expected performance against combined scenarios of socioeconomic and climate change. Based upon stakeholder feedback, a reference scenario representing current trends in climate and socioeconomic drivers (‘business-as-usual’) was used as a dynamic baseline against which to compare results of other scenarios. The robustness of response options was evaluated by their utility in different environmental and social contexts as represented by the scenarios, and linked to their adaptability to adjust to changing conditions. Key findings demonstrate that adaptability becomes increasingly valuable as the magnitude and rate of future change diverges from current trends. Stress-testing also revealed that individual responses in isolation are unlikely to be robust meaning there are advantages from integrating cohesive combinations (bundles) of response options to maximise their individual strengths and compensate for weaknesses. This identifies a role for both top-down and bottom-up responses, including regulation, spatial targeting, incentives and partnership initiatives, and their use in combination through integrated assessment and planning consistent with the adoption of an Ecosystem Approach. Stress-testing approaches can have an important role in future-proofing policy appraisals but important knowledge gaps remain, especially for cultural and supporting ecosystem services. Finally, barriers and enablers to the implementation of more integrated long-term adaptive responses were identified drawing on the ‘4 Is’ (Institutions, Information, Incentives, Identity) conceptual framework. This highlighted the crucial but usually understated role of identity in promoting ownership and uptake of responses

The role of information in multi-agent learning

This paper aims to contribute to the study of auction design within the domain of agent-based computational economics. In particular, we investigate the efficiency of different auction mechanisms in a bounded-rationality setting where heterogeneous artificial agents learn to compete for the supply of a homogeneous good. Two different auction mechanisms are compared: the uniform and the discriminatory pricing rules. Demand is considered constant and inelastic to price. Four learning algorithms representing different models of bounded rationality, are considered for modeling agents' learning capabilities. Results are analyzed according to two game-theoretic solution concepts, i.e., Nash equilibria and Pareto optima, and three performance metrics. Different computational experiments have been performed in different game settings, i.e., self-play and mixed-play competition with two, three and four market participants. This methodological approach permits to highlight properties which are invariant to the different market settings considered. The main economic result is that, irrespective of the learning model considered, the discriminatory pricing rule is a more e±cient market mechanism than the uniform one in the two and three players games, whereas identical outcomes are obtained in four players competitions. Important insights are also given for the use of multi-agent learning as a framework for market design.multi-agent learning; auction markets; design economics; agent-based computational economics

The Influence of Experimental and Computational Economics: Economics Back to the Future of Social Sciences

Economics has been a most puzzling science, namely since the neoclassical revolution defined the legitimate procedures for theorisation and quantification. Its epistemology has based on farce: decisive tests are not applied on dare predictions. As a consequence, estimation has finally been replaced by simulation, and empirical tests have been substituted by non-disciplined exercises of comparison of models with reality. Furthermore, the core concepts of economics defy the normally accepted semantics and tend to establish meanings of their own. One of the obvious instances is the notion of rationality, which has been generally equated with the apt use of formal logic or the ability to apply econometric estimation as a rule of thumb for daily life. In that sense, rationality is defined devoid of content, as alien to the construction of significance and reference by reason and social communication. The contradictory use of simulacra and automata, by John von Neumann and Herbert Simon, was a response to this escape of economic models from reality, suggesting that markets could be conceived of as complex institutions. But most mainstream economists did not understand or did not accept these novelties, and the empirical inquiry or the realistic representation of the action of agents and of their social interaction remained a minor domain of economics, and was essentially ignored by canonical theorizing. The argument of the current paper is based on a survey and discussion of the twin contributions of experimental and computational economics to these issues. Although mainly arising out of the mainstream, these emergent fields of economics generate challenging heuristics as well as new empirical results that defy orthodoxy. Their contributions both to the definition of the social meanings of rationality and to the definition of a new brand of inductive economics are discussed.

Introduction to \u3cem\u3eThe Economics of Social Institutions\u3c/em\u3e

This volume includes thirty-six important contributions to the economics of social institutions by leading figures in the history of the field. Its nine Parts are: Early Contributions, Methodological and Conceptual Issues, Old Institutionalism, New Institutionalism, Social Costs, Growth and Development, Institutions and Change, Institutions and Organizations, and The Third Sphere of the Economy and Institutions. This set of topics provides a comprehensive review of the origins and development of the economics of social institutions. It addresses the main theoretical and policy concerns that have occupied contributors to the approach. The economics of social institutions has a been well-established research program for over a century, and continues to evolve and develop new areas of investigation. This collection provides researchers, scholars, and interested students and extensive review of the leading contributions to the subject. It can be used to advance future thinking about the economics of social institutions and as a key resource for university teaching and education

The role of information in multi-agent learning

This paper aims to contribute to the study of auction design within the domain of agent-based computational economics. In particular, we investigate the efficiency of different auction mechanisms in a bounded-rationality setting where heterogeneous artificial agents learn to compete for the supply of a homogeneous good. Two different auction mechanisms are compared: the uniform and the discriminatory pricing rules. Demand is considered constant and inelastic to price. Four learning algorithms representing different models of bounded rationality, are considered for modeling agents' learning capabilities. Results are analyzed according to two game-theoretic solution concepts, i.e., Nash equilibria and Pareto optima, and three performance metrics. Different computational experiments have been performed in different game settings, i.e., self-play and mixed-play competition with two, three and four market participants. This methodological approach permits to highlight properties which are invariant to the different market settings considered. The main economic result is that, irrespective of the learning model considered, the discriminatory pricing rule is a more e±cient market mechanism than the uniform one in the two and three players games, whereas identical outcomes are obtained in four players competitions. Important insights are also given for the use of multi-agent learning as a framework for market design

Holding on for too long? An experimental study on inertia in entrepreneurs’ and non-entrepreneurs’ disinvestment choices

Disinvestment, in the sense of project termination and liquidation of assets including the cession of a venture, is an important realm of entrepreneurial decision-making. This study presents the results of an experimental investigation modeling the choice to disinvest as a dynamic problem of optimal stopping in which the patterns of decisions are analyzed with entrepreneurs and non-entrepreneurs. Our experimental results reject the standard net present value approach as an account of observed behavior. Instead, most individuals seem to understand the value of waiting. Their choices are weakly related to the disinvestment triggers derived from a formal optimal stopping benchmark consistent with real options reasoning. We also observe a pronounced ‘psychological inertia’, i.e., most individuals hold on to a losing project for even longer than real options reasoning would predict. The study provides evidence for entrepreneurs and non-entrepreneurs being quite similar in their behavior.Real-Options, Disinvestment, Exit Behavior, Experimental Economics, Agribusiness, Agricultural and Food Policy, Agricultural Finance, Institutional and Behavioral Economics,