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  1. Bruce Edmonds, A Brief Survey of Some Results on Mechanisms and Emergent Outcomes.
    The mechanisms/abilities of agents compared to the emergent outcomes in three different scenarios from my past work is summarised: the El Farol Game; an Artificial Stock Market; and the Iterated Prisoner’s Dilemma. Within each of these, the presence or absence of some different agent abilities was examined, the results being summarised here – some turning out to be necessary, some not. The ability in terms of the recognition of other agents, either by characteristics or by name is a recurring theme. (...)
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  2. Bruce Edmonds, Syntactic Measures of Complexity.
    1.1 - Background - page 17 1.2 - The Style of Approach - page 18 1.3 - Motivation - page 19 1.4 - Style of Presentation - page 20 1.5 - Outline of the Thesis - page 21..
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  3. Bruce Edmonds, When and Why Does Haggling Occur?
    We present a computational simulation which captures aspects of negotiation as the interaction of agents searching for an agreement over their own mental model. Specifically this simulation relates the beliefs of each agent about the action of cause and effect to the resulting negotiation dialogue. The model highlights the difference between negotiating to find any solution and negotiating to obtain the best solution from the point of view of each agent. The later case corresponds most closely to what is commonly (...)
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  4. Bruce Edmonds, What If All Truth is Context-Dependent?
    This paper argues that truth is by nature context-dependent – that no truth can be applied regardless of context. I call this “strong contextualism”. Some objections to this are considered and rejected, principally: that there are universal truths given to us by physics, logic and mathematics; and that claiming “no truths are universal” is self-defeating. Two “models” of truth are suggested to indicate that strong contextualism is coherent. It is suggested that some of the utility of the “universal framework” can (...)
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  5. Michael Wooldridge & Bruce Edmonds, Reasoning About Rational Agents.
    what is now the mainstream view as to the best way forward in the dream of engineering reliable software systems out of autonomous agents. The way of using formal logics to specify, implement and verify distributed systems of interacting units using a guiding analogy of beliefs, desires and intentions. The implicit message behind the book is this: Distributed Artificial Intelligence (DAI) can be a respectable engineering science. It says: we use sound formal systems; can cite established philosophical foundations; and will (...)
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  6. Bruce Edmonds, Achieving Consensus Among Agents - an Opinion-Dynamics Model.
    The paper considers the problem of how a distributed system of agents (who communicate only via a localised network) might achieve consensus by copying beliefs (copy) from each other and doing some belief pruning themselves (drop). This is explored using a social simulation model, where beliefs interact with each other via a compatibility function, which assigns a level of compatibility (which is a sort of weak consistency) to a set of beliefs. The probability of copy and drop processes occurring is (...)
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  7. Bruce Edmonds, Against Prior Theorising.
    Prior theory – that is theorising on the basis of thought and intuition , as opposed to attempting to explain observed data – inevitably distorts what comes after. It biases us in the selection of our data (the data model) and certainly biases any theorising that follows. It does this because we (as humans) can not help but see the world through our theorising – we are blind without the theoretical “spectacles” described by Kuhn (1962). If a theory has shown (...)
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  8. Bruce Edmonds, Agent-Based Social Simulation and its Necessity for Understanding Socially Embedded Phenomena.
    Some issues and varieties of computational and other approaches to understanding socially embedded phenomena are discussed. It is argued that of all the approaches currently available, only agent-based simulation holds out the prospect for adequately representing and understanding phenomena such as social norms.
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  9. Bruce Edmonds, Bootstrapping Knowledge About Social Phenomena Using Simulation Models.
    Formidable difficulties face anyone trying to model social phenomena using a formal system, such as a computer program. The differences between formal systems and complex, multi-facetted and meaning-laden social systems are so fundamental that many will criticise any attempt to bridge this gap. Despite this, there are those who are so bullish about the project of social simulation that they appear to believe that simple computer models, that are also useful and reliable indicators of how aspects of society works, are (...)
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  10. Bruce Edmonds, Gossip, Sexual Recombination and the El Farol Bar: Modelling the Emergence of Heterogeneity.
    An investigation into the conditions conducive to the emergence of heterogeneity amoung agents is presented. This is done by using a model of creative artificial agents to investigate some of the possibilities. The simulation is based on Brian Arthur's 'El Farol Bar' model but extended so that the agents also learn and communicate. The learning and communication is implemented using an evolutionary process acting upon a population of strategies inside each agent. This evolutionary learning process is based on a Genetic (...)
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  11. Bruce Edmonds, Indeterminacy: The Mapped, the Navigable, and the Uncharted.
    Determinism is the thesis that a future state is completely determined by a past state of something - thus its future course is fixed when the initial state is given. Before the discovery of quantum mechanics many people thought the universe was deterministic; rather like a huge clock. Indeterminacy is when something is NOT deterministic, that is the initial state does not completely determine all subsequent ones. Indeterminacy is an important topic and doubly so for those involved in social simulation. (...)
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  12. Bruce Edmonds, Learning and Exploiting Context in Agents.
    The use of context can considerably facilitate reasoning by restricting the beliefs reasoned upon to those relevant and providing extra information specific to the context. Despite the use and formalization of context being extensively studied both in AI and ML, context has not been much utilized in agents. This may be because many agents are only applied in a single context, and so these aspects are implicit in their design, or it may be that the need to explicitly encode information (...)
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  13. Bruce Edmonds, Model-to-Model Analysis.
    In recent years there has been an explosion of published literature utilising Multi-Agent-Based Simulation (MABS) to study social, biological and artificial systems. This kind of work is evidenced within JASSS but is increasingly becoming part of mainstream practice across many disciplines.
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  14. Bruce Edmonds, Modelling Socially Intelligent Agents.
    The perspective of modelling agents rather than using them for a specificed purpose entails a difference in approach. In particular an emphasis on veracity as opposed to efficiency. An approach using evolving populations of mental models is described that goes some way to meet these concerns. It is then argued that social intelligence is not merely intelligence plus interaction but should allow for individual relationships to develop between agents. This means that, at least, agents must be able to distinguish, identify, (...)
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  15. Bruce Edmonds, Open Access for Social Simulation.
    We consider here issues of open access to social simulations, with a particular focus on software licences, though also briefly discussing documentation and archiving. Without any specific software licence, the default arrangements are stipulated by the Berne Convention (for those countries adopting it), and are unsuitable for software to be used as part of the scientific process (i.e. simulation software used to generate conclusions that are to be considered part of the scientific domain of discourse). Without stipulating any specific software (...)
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  16. Bruce Edmonds, Reasoning About Rational Agents.
    This book is an archetypal product of the Belief-Desire-Intention (BDI) school of multi-agent systems. It presents what is now the mainstream view as to the best way forward in the dream of engineering reliable software systems out of autonomous agents. The way of using formal logics to specify, implement and verify distributed systems of interacting units using a guiding analogy of beliefs, desires and intentions. The implicit message behind the book is this: Distributed Artificial Intelligence (DAI) can be a respectable (...)
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  17. Bruce Edmonds, Replication, Replication and Replication: Some Hard Lessons From Model Alignment.
    A published simulation model Riolo et al. 2001 ) was replicated in two independent implementations so that the results as well as the conceptual design align. This double replication allowed the original to be analysed and critiqued with confidence. In this case, the replication revealed some weaknesses in the original model, which otherwise might not have come to light. This shows that unreplicated simulation models and their results can not be trusted - as with other kinds of experiment, simulations need (...)
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  18. Bruce Edmonds, SDML: A Multi-Agent Language for Organizational Modelling.
    The SDML programming language which is optimized for modelling multi-agent interaction within articulated social structures such as organizations is described with several examples of its functionality. SDML is a strictly declarative modelling language which has object-oriented features and corresponds to a fragment of strongly grounded autoepistemic logic. The virtues of SDML include the ease of building complex models and the facility for representing agents flexibly as models of cognition as well as modularity and code reusability.
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  19. Bruce Edmonds, Simplicity is Not Truth-Indicative.
    In this paper I will argue that, in general, where the evidence supports two theories equally, the simpler theory is not more likely to be true and is not likely to be nearer the truth. In other words simplicity does not tell us anything about model bias. Our preference for simpler theories (apart from their obvious pragmatic advantages) can be explained by the facts that humans are known to elaborate unsuccessful theories rather than attempt a thorough revision and that a (...)
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  20. Bruce Edmonds, 13 Short Poems of Limitation and Loss.
    It is a lie: nature is not balanced, but tumbling forwards in a damp confusion of forms. Not so much a comforting friend as a science-fiction monster: adsorbing all the bullets we shoot at it – each time getting up and coming back at us; each time further mutated and more terrifying.
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  21. Bruce Edmonds, The Collection of Computer Science Bibliographies.
    The main search engine has been changed (including RSS queries). The default logic operator is "OR" now (may look strange when results are sorted by year). Please report all noticed errors, misfeatures or omissions you notice. Thanks!
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  22. Bruce Edmonds, The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies.
    This book is an argument for the importance of diversity in society. It is not naive, in the sense that it does not argue that any diversity is helpful, but rather tries to distinguish some of the ways in which it can be helpful and, hence, some the conditions under which it can be helpful. It does this is a largely non technical language and using informal argument using argument, examples and a review of the evidence to support its conclusions. (...)
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  23. Bruce Edmonds, The Emergence of Symbiotic Groups Resulting From Skill-Differentiation and Tags.
    This paper presents a evolutionary simulation where the presence of 'tags' and an inbuilt specialisation in terms of skills result in the development of 'symbiotic' sharing within groups of individuals with similar tags. It is shown that the greater the number of possible sharing occasions there are the higher the population that is able to be sustained using the same level of resources. The 'life-cycle' of a particular cluster of tag-groups is illustrated showing: the establishment of sharing; a focusing-in of (...)
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  24. Bruce Edmonds, Towards Good Social Science.
    The paper investigates what is meant by "good science" and "bad science" and how these differ as between the natural (physical and biological) sciences on the one hand and social sciences on the other. We conclude on the basis of historical evidence that the natural science are much more heavily constrained by evidence and observation than by theory while the social sciences are constrained by prior theory and hardly at all by direct evidence. Current examples of the latter proposition are (...)
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  25. Bruce Edmonds, The Insufficiency of Formal Design Methods.
    We highlight the limitations of formal methods by exhibiting two results in recursive function theory: that there is no effective means of finding a program that satisfies a given formal specification; or checking that a program meets a specification. We also exhibit a ‘simple’ MAS which has all the power of a Turing machine. We then argue that any ‘pure design’ methodology will face insurmountable difficulties in today’s open and complex MAS. Rather we suggest a methodology based on the classic (...)
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  26. Bruce Edmonds, The Purpose and Place of Formal Systems in the Development of Science.
    The aim of this paper is to re-emphasise that the purpose of formal systems is to provide something to map into and to stem the tide of unjustified formal systems. I start by arguing that expressiveness alone is not a sufficient justification for a new formal system but that it must be justified on pragmatic grounds. I then deal with a possible objection as might be raised by a pure mathematician and after that to the objection that theory can be (...)
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  27. Bruce Edmonds, The Social Embedding of Intelligence.
    I claim that in order to pass the Turing Test over any period of extended time, it will necessary to embed the entity into society. This chapter discusses why this is, and how it might be brought about. I start by arguing that intelligence is better characterised by tests of social interaction, especially in open-ended and extended situations. I then argue that learning is an essential component of intelligence and hence that a universal intelligence is impossible. These two arguments support (...)
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  28. Bruce Edmonds, The Use of Models.
    The use of MABS (Multi-Agent Based Simulations) is analysed as the modelling of distributed (usually social) systems using MAS as the model structure. It is argued that rarely is direct modelling of target systems attempted but rather an abstraction of the target systems is modelled and insights gained about the abstraction then applied back to the target systems. The MABS modelling process is divided into six steps: abstraction, design, inference, analysis, interpretation and application. Some types of MABS papers are characterised (...)
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  29. Bruce Edmonds, The Uses of Genetic Programming in Social Simulation: A Review of Five Books. [REVIEW]
    Genetic Programming (GP) is a technique which permits automatic search for complex solutions using a computer. It goes beyond previous techniques in that it discovers the structure of those solutions. Previously, if one were trying to find an equation to fit a set of data, one would have had to provide the form of the equation (for example a fourth degree polynomial) and the computer could then find the appropriate parameters. By contrast, GP can experiment with a whole range of (...)
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  30. Bruce Edmonds, Understanding Observed Complex Systems – the Hard Complexity Problem.
    bruce@edmonds.name http://bruce.edmonds.name Abstract. Two kinds of problem are distinguished: the first of finding processes which produce complex outcomes from the interaction of simple parts, and the second of finding which process resulted in an observed complex outcome. The former I call the easy complexity problem and the later the hard complexity problem. It is often assumed that progress with the easy problem will aid process with the hard problem. However this assumes that the “reverse engineering” problem, of determining the process (...)
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  31. Bruce Edmonds, When and Why Does Haggling Occur? Some Suggestions From a Qualitative but Computational Simulation of Negotiation.
    We present a computational simulation which captures aspects of negotiation as the interaction of agents searching for an agreement over their own mental model. Specifically this simulation relates the beliefs of each agent about the action of cause and effect to the resulting negotiation dialogue. The model highlights the difference between negotiating to find any solution and negotiating to obtain the best solution from the point of view of each agent. The later case corresponds most closely to what is commonly (...)
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  32. Ruth Meyer & Bruce Edmonds, Signatures in Networks Generated From Agent-Based Social Simulation Models.
    Finding suitable analysis techniques for networks generated from social processes is a difficult task when the population changes over time. Traditional social network analysis measures may not work in such circumstances. It is argued that agent-based social networks should not be constrained by a priori assumptions about the evolved network and/or the analysis techniques. In most agent-based social simulation models, the number of agents remains fixed throughout the simulation; this paper considers the case when this does not hold. Thus the (...)
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  33. Bruce Edmonds (2013). Complexity and Context-Dependency. Foundations of Science 18 (4):745-755.
    It is argued that given the “anti-anthropomorphic” principle—that the universe is not structured for our benefit—modelling trade-offs will necessarily mean that many of our models will be context-specific. It is argued that context-specificity is not the same as relativism. The “context heuristic”—that of dividing processing into rich, fuzzy context-recognition and crisp, conscious reasoning and learning—is outlined. The consequences of accepting the impact of this human heuristic in the light of the necessity of accepting context-specificity in our modelling of complex systems (...)
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  34. Bruce Edmonds & Carlos Gershenson (2012). Learning, Social Intelligence and the Turing Test. In. In S. Barry Cooper (ed.), How the World Computes. 182--192.
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  35. Bruce Edmonds (2011). Disaggregating Quality Judgements. Mind and Society 10 (2):169-180.
    The notion of quality is analysed for its functional roots as a social heuristic for reusing others’ quality judgements and hence aiding choice. This is applied to the context of academic publishing, where the costs of publishing have greatly decreased, but the problem of finding the papers one wants has become harder. This paper suggests that instead of relying on generic quality judgements, such as those delivered by journal reviewers, that the maximum amount of judgemental information be preserved and then (...)
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  36. Bruce Edmonds (2004). Implementing Free Will. In D. N. Davis (ed.), Visions of Mind: Architectures for Cognition and Affect. IDEA Group Publishing.
    “The demonstration that no possible combination of known substances, known forms of machinery and known forms of force, can be united in a practical machine by which man shall fly long distances through the air, seems to the writer as complete as it is possible for the demonstration of any physical fact to be.” Simon Newcomb, Professor of Mathematics, John Hopkins University, 1901 Abstract Free will is described in terms of the useful properties that it could confer, explaining why it (...)
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  37. Bruce Edmonds & Varol Akman (2002). Editorial: Context in Context. [REVIEW] Foundations of Science 7 (3):233-238.
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  38. Bruce Edmonds (2000). Complexity and Scientific Modelling. Foundations of Science 5 (3):379-390.
    It is argued that complexity is not attributable directly to systems or processes but rather to the descriptions of their `best' models, to reflect their difficulty. Thus it is relative to the modelling language and type of difficulty. This approach to complexity is situated in a model of modelling. Such an approach makes sense of a number of aspects of scientific modelling: complexity is not situated between order and disorder; noise can be explicated by approaches to excess modelling error; and (...)
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  39. Bruce Edmonds (2000). The Constructability of Artificial Intelligence (as Defined by the Turing Test). Journal of Logic Language and Information 9 (4):419-424.
    The Turing Test (TT), as originally specified, centres on theability to perform a social role. The TT can be seen as a test of anability to enter into normal human social dynamics. In this light itseems unlikely that such an entity can be wholly designed in anoff-line mode; rather a considerable period of training insitu would be required. The argument that since we can pass the TT,and our cognitive processes might be implemented as a Turing Machine(TM), that consequently (...)
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  40. Bruce Edmonds, Towards Implementing Free-Will.
    Some practical criteria for free-will are suggested where free-will is a matter of degree. It is argued that these are more appropriate than some extremely idealised conceptions. Thus although the paper takes lessons from philosophy it avoids idealistic approaches as irrelevant. A mechanism for allowing an agent to meet these criteria is suggested: that of facilitating the gradual emergence of free-will in the brain via an internal evolutionary process. This meets the requirement that not only must the choice of action (...)
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  41. Bruce Edmonds (1999). Pragmatic Holism (or Pragmatic Reductionism). Foundations of Science 4 (1):57-82.
    The reductionist/holist debate is highly polarised. I propose an intermediate position of pragmatic holism. It derives from two claims: firstly, that irrespective of whether all natural systems are theoretically reducible, for many systems it is utterly impractical to attempt such a reduction, and secondly, that regardless of whether irreducible 'wholes exist, it is vain to try and prove this. This position illuminates the debate along new pragmatic lines by refocussing attention on the underlying heuristics of learning about the natural world.
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  42. Bruce Edmonds, The Pragmatic Roots of Context.
    When modelling complex systems one can not include all the causal factors, but one has to settle for partial models. This is alright if the factors left out are either so constant that they can be ignored or one is able to recognise the circumstances when they will be such that the partial model applies. The transference of knowledge from the point of application to the point of learning utilises a combination of recognition and inference ­ a simple model of (...)
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  43. Bruce Edmonds (1996). Pragmatic Holism. Foundations of Science 4 (1):57-82.
    The reductionist/holist debate seems an impoverished one, with many participants appearing to adopt a position first and constructing rationalisations second. Here I propose an intermediate position of pragmatic holism, that irrespective of whether all natural systems are theoretically reducible, for many systems it is completely impractical to attempt such a reduction, also that regardless if whether irreducible `wholes' exist, it is vain to try and prove this in absolute terms. This position thus illuminates the debate along new pragmatic lines, and (...)
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  44. Scott Moss & Bruce Edmonds (1994). Modelling Learning as Modelling. .
    Economists tend to represent learning as a procedure for estimating the parameters of the "correct" econometric model. We extend this approach by assuming that agents specify as well as estimate models. Learning thus takes the form of a dynamic process of developing models using an internal language of representation where expectations are formed by forecasting with the best current model. This introduces a distinction between the form and content of the internal models which is particularly relevant for boundedly rational agents. (...)
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