Introduction to the special issue on neural networks in financial engineering

There are several phases that an emerging field goes through before it reaches maturity, and computational finance is no exception. There is usually a trigger for the birth of the field. In our case, new techniques such as neural networks, significant progress in computing technology, and the need for results that rely on more realistic assumptions inspired new researchers to revisit the traditional problems of finance, problems that have often been tackled by introducing simplifying assumptions in the past. The result has been a wealth of new approaches to these time-honored problems, with significant improvements in many cases

New perspectives on realism, tractability, and complexity in economics

Fuzzy logic and genetic algorithms are used to rework more realistic (and more complex) models of competitive markets. The resulting equilibria are significantly different from the ones predicted from the usual static analysis; the methodology solves the Walrasian problem of how markets can reach equilibrium, starting with firms trading at disparate prices. The modified equilibria found in these complex market models involve some mutual self-restraint on the part of the agents involved, relative to economically rational behaviour. Research (using similar techniques) into the evolution of collaborative behaviours in economics, and of altruism generally, is summarized; and the joint significance of these two bodies of work for public policy is reviewed. The possible extension of the fuzzy/ genetic methodology to other technical aspects of economics (including international trade theory, and development) is also discussed, as are the limitations to the usefulness of any type of theory in political domains. For the latter purpose, a more differentiated concept of rationality, appropriate to ill-structured choices, is developed. The philosophical case for laissez-faire policies is considered briefly; and the prospects for change in the way we ‘do economics’ are analysed

The Development of Social Simulation as Reflected in the First Ten Years of JASSS: a Citation and Co-Citation Analysis

Social simulation is often described as a multidisciplinary and fast-moving field. This can make it difficult to obtain an overview of the field both for contributing researchers and for outsiders who are interested in social simulation. The Journal for Artificial Societies and Social Simulation (JASSS) completing its tenth year provides a good opportunity to take stock of what happened over this time period. First, we use citation analysis to identify the most influential publications and to verify characteristics of social simulation such as its multidisciplinary nature. Then, we perform a co-citation analysis to visualize the intellectual structure of social simulation and its development. Overall, the analysis shows social simulation both in its early stage and during its first steps towards becoming a more differentiated discipline.Citation Analysis, Co-Citation Analysis, Lines of Research, Multidisciplinary, Science Studies, Social Simulation

Reviewing agent-based modelling of socio-ecosystems: a methodology for the analysis of climate change adaptation and sustainability

The integrated - environmental, economic and social - analysis of climate change calls for a paradigm shift as it is fundamentally a problem of complex, bottom-up and multi-agent human behaviour. There is a growing awareness that global environmental change dynamics and the related socio-economic implications involve a degree of complexity that requires an innovative modelling of combined social and ecological systems. Climate change policy can no longer be addressed separately from a broader context of adaptation and sustainability strategies. A vast body of literature on agent-based modelling (ABM) shows its potential to couple social and environmental models, to incorporate the influence of micro-level decision making in the system dynamics and to study the emergence of collective responses to policies. However, there are few publications which concretely apply this methodology to the study of climate change related issues. The analysis of the state of the art reported in this paper supports the idea that today ABM is an appropriate methodology for the bottom-up exploration of climate policies, especially because it can take into account adaptive behaviour and heterogeneity of the system's components.Review, Agent-Based Modelling, Socio-Ecosystems, Climate Change, Adaptation, Complexity.

New perspectives on realism, tractability, and complexity in economics

Fuzzy logic and genetic algorithms are used to rework more realistic (and more complex) models of competitive markets. The resulting equilibria are significantly different from the ones predicted from the usual static analysis; the methodology solves the Walrasian problem of how markets can reach equilibrium, starting with firms trading at disparate prices. The modified equilibria found in these complex market models involve some mutual self-restraint on the part of the agents involved, relative to economically rational behaviour. Research (using similar techniques) into the evolution of collaborative behaviours in economics, and of altruism generally, is summarized; and the joint significance of these two bodies of work for public policy is reviewed. The possible extension of the fuzzy/ genetic methodology to other technical aspects of economics (including international trade theory, and development) is also discussed, as are the limitations to the usefulness of any type of theory in political domains. For the latter purpose, a more differentiated concept of rationality, appropriate to ill-structured choices, is developed. The philosophical case for laissez-faire policies is considered briefly; and the prospects for change in the way we ‘do economics’ are analysed.Fuzzy logic; genetic algorithms; complexity; emergence; rationality; ill-structured choice; equilibrium; Walrasian Crier; paradigm change;

The Effects of the Quantification of Faculty Productivity: Perspectives from the Design Science Research Community

In recent years, efforts to assess faculty research productivity have focused more on the measurable quantification of academic outcomes. For benchmarking academic performance, researchers have developed different ranking and rating lists that define so-called high-quality research. While many scholars in IS consider lists such as the Senior Scholar’s basket (SSB) to provide good guidance, others who belong to less-mainstream groups in the IS discipline could perceive these lists as constraining. Thus, we analyzed the perceived impact of the SSB on information systems (IS) academics working in design science research (DSR) and, in particular, how it has affected their research behavior. We found the DSR community felt a strong normative influence from the SSB. We conducted a content analysis of the SSB and found evidence that some of its journals have come to accept DSR more. We note the emergence of papers in the SSB that outline the role of theory in DSR and describe DSR methodologies, which indicates that the DSR community has rallied to describe what to expect from a DSR manuscript to the broader IS community and to guide the DSR community on how to organize papers for publication in the SSB