Competitive multi-agent search

textWhile evolutionary computation is well suited for automatic discovery in engineering, it can also be used to gain insight into how humans and organizations could perform more effectively. Using a real-world problem of innovation search in organizations as the motivating example, this dissertation formalizes human creative problem solving as competitive multi-agent search. It differs from existing single-agent and team-search problems in that the agents interact through knowledge of other agents' searches and through the dynamic changes in the search landscape caused by these searches. The main hypothesis is that evolutionary computation can be used to discover effective strategies for competitive multi-agent search. This hypothesis is verified in experiments using an abstract domain based on the NK model, i.e. partially correlated and tunably rugged fitness landscapes, and a concrete domain in the form of a social innovation game. In both domains, different specialized strategies are evolved for each different competitive environment, and also strategies that generalize across environments. Strategies evolved in the abstract domain are more effective and more complex than hand-designed strategies and one based on traditional tree search. Using a novel spherical visualization of the fitness landscapes of the abstract domain, insight is gained about how successful strategies work, e.g. by tracking positive changes in the landscape. In the concrete game domain, human players were modeled using backpropagation, and used as opponents to create environments for evolution. Evolved strategies scored significantly higher than the human models by using a different proportion of actions, providing insights into how performance could be improved in social innovation domains. The work thus provides a possible framework for studying various human creative activities as competitive multi-agent search in the future.Computer Science

Evolution: Complexity, uncertainty and innovation

Complexity science provides a general mathematical basis for evolutionary thinking. It makes us face the inherent, irreducible nature of uncertainty and the limits to knowledge and prediction. Complex, evolutionary systems work on the basis of on-going, continuous internal processes of exploration, experimentation and innovation at their underlying levels. This is acted upon by the level above, leading to a selection process on the lower levels and a probing of the stability of the level above. This could either be an organizational level above, or the potential market place. Models aimed at predicting system behaviour therefore consist of assumptions of constraints on the micro-level – and because of inertia or conformity may be approximately true for some unspecified time. However, systems without strong mechanisms of repression and conformity will evolve, innovate and change, creating new emergent structures, capabilities and characteristics. Systems with no individual freedom at their lower levels will have predictable behaviour in the short term – but will not survive in the long term. Creative, innovative, evolving systems, on the other hand, will more probably survive over longer times, but will not have predictable characteristics or behaviour. These minimal mechanisms are all that are required to explain (though not predict) the co-evolutionary processes occurring in markets, organizations, and indeed in emergent, evolutionary communities of practice. Some examples will be presented briefly

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.

Agent-Based Computational Economics

Agent-based computational economics (ACE) is the computational study of economies modeled as evolving systems of autonomous interacting agents. Starting from initial conditions, specified by the modeler, the computational economy evolves over time as its constituent agents repeatedly interact with each other and learn from these interactions. ACE is therefore a bottom-up culture-dish approach to the study of economic systems. This study discusses the key characteristics and goals of the ACE methodology. Eight currently active research areas are highlighted for concrete illustration. Potential advantages and disadvantages of the ACE methodology are considered, along with open questions and possible directions for future research.Agent-based computational economics; Autonomous agents; Interaction networks; Learning; Evolution; Mechanism design; Computational economics; Object-oriented programming.

Ontology acquisition and exchange of evolutionary product-brokering agents

Agent-based electronic commerce (e-commerce) has been booming with the development of the Internet and agent technologies. However, little effort has been devoted to exploring the learning and evolving capabilities of software agents. This paper addresses issues of evolving software agents in e-commerce applications. An agent structure with evolution features is proposed with a focus on internal hierarchical knowledge. We argue that knowledge base of agents should be the cornerstone for their evolution capabilities, and agents can enhance their knowledge bases by exchanging knowledge with other agents. In this paper, product ontology is chosen as an instance of knowledge base. We propose a new approach to facilitate ontology exchange among e-commerce agents. The ontology exchange model and its formalities are elaborated. Product-brokering agents have been designed and implemented, which accomplish the ontology exchange process from request to integration

Towards the Framing of Venture Capital Policies: a Systems-Evolutionary Perspective with Particular Reference to the UK/Scotland and Israeli Experiences

We compare some of the policies that have been attempted in Europe (UK/Scotland) and Israel over the past fifteen years to elaborate a new Systems Evolutionary (SE) framework for rethinking VC policy and related ITP. We argue that this perspective is useful for both real world (‘positive’) analysis and policy (‘normative’) analys is. Our SE framework is shaped by (i) a multidimensional view of VC; (ii) strong between VC, VC policy and the development of EHTCs; and (iii) a strategic approach to policy. In contrast, many VC policies in Europe up to and including the 1990s took a ‘static’ financial view of VC that focused on ‘bridging existng early phase finance gaps of innovative companies’ rather than creating of a new mechanism to assure the timely growth of EHTCs. We aim to present the new framework rather than to provide specific recommendations. The main conclusion is that the success of VC policies depend on factors such as the phase of evolution of (i) VC or related innovation finance organizations; (ii) the underlying segment of start up companies and of high tech industries; (iii) the specific country/region institutional setting. While in some contexts it may be worth considering the targeting of a new VC industry/market (and associated EHTC) in others the focus of policy should center in improving pre-emergence conditions. More specifically it may be, given that VC searches for ‘investment ready opportunities’, that ITP should, in many contexts, precede VC policies. Another key conclusion is that implementing this perspective necessitates the creation of a strategic level of policy, with a view of specifying a set of strategic priorities for Scie nce, Technology, and Innovation, priorities that should precede rather than follow policy design and implementation. A major challenge is to extend the present framework that was initially based on VCs oriented towards ICT to LS.

Agent Technology in Supply Chains and Networks: An exploration of high potential future applications

This paper reports on an ongoing research project that\ud is aimed at evaluating how software agents can improve\ud performance of supply chains and networks. To conduct\ud this evaluation, first a framework is developed to classify\ud potential applications of software agents to supply\ud networks. The framework was used in workshop sessions\ud with logistics and information systems experts from\ud industry, software/consultancy and academia to identify\ud promising areas for agents. Based on the framework and\ud the outcome of the workshop sessions, this paper presents\ud promising application areas for the near future and\ud beyond