The observance of unpredictable episodes of clustered volatility in some data series has led to the development of models of social processes that will give rise to such clustered volatility. Such models are not, however, validated directly against qualitative evidence about the behaviour of individuals and how they interact. An agent based simulation model of the effect of drought on domestic water consumption is reported here that is the outcome of a process of development involving stakeholders to inform and validate the model qualitatively at micro level while ensuring that numerical outputs from the model cohere with observed time series data. We argue that this cross-validation of agent based social simulation models is a significant advancement in the analysis of social process.

Abstract In this paper a genetic algorithm is proposed where the worst individual and individuals with indices close to its index are replaced in every generation by randomly generated individuals for dynamic optimization problems. In the proposed genetic algorithm, the replacement of an individual can affect other individuals in a chain reaction. The new individuals are preserved in a subpopulation which is defined by the number of individuals created in the current chain reaction. If the values of fitness are similar, as is the case with small diversity, one single replacement can affect a large number of individuals in the population. This simple approach can take the system to a self-organizing behavior, which can be useful to control the diversity level of the population and hence allows the genetic algorithm to escape from local optima once the problem changes due to the dynamics.

The Sackler Colloquium on on Adaptive Agents, Intelligence and Emergent Human Organization is intended to force a re-examination of current social theory by means of an exploration of adaptive agent models. The primary issue addressed by this paper is why it should be assumed that adaptive agent models are suitable for that purpose. The argument is predicated on the view that social science should start with observation and the specification of a problem to be solved and then reason from that basis to define the appropriate properties and conditions of application of relevant tools of analysis. Accordingly, evidence is adduced from data for sales volumes and values of a disparate range of goods to show that, in all presented cases, frequency distributions are fat tailed. This result implies that, if there is a suitable and stable population distribution, it will generally have infinite variance and perhaps undefined mean. Agent based models with agents that reason about their behaviour and specifically do not conform to the rational choice model and are influenced by, but do not imitate, other agents known to them will typically generate fat tailed time series data. There is no reason to suppose that this data is drawn from any stable distribution. An agent based social simulation model of intermediated exchange is reported that has the same type fat tailed time series and cross sectional data that is found in data for fast moving consumer goods and for retail outlets. This result supports the proposition that adaptive agent models of markets with agents that reason and are socially embedded have the same statistical signatures as real markets. This statistical signature precludes any conventional hypothesis testing or forecasting -- a point which pertains both to inte...

In this chapter, I review the main methods and techniques of complex systems science. As a

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Richardson’s finding that the severity of interstate wars is power-law distributed belongs to the most striking empirical regularities in world politics. Yet, this is a regularity in search for a theory. Drawing on the principles of self-organized criticality, I propose an agent-based model of war and state-formation that exhibits power-law regularities. The computational findings suggest that the scale-free behavior depends on a process of technological change that leads to contextually-dependent, stochastic decisions to wage war. *) Earlier drafts of this paper were prepared for presentation at the University of Michigan, University of Chicago, Ohio State University, Yale University and Pennsylvania University. I am grateful to the participants of those meetings and to Robert Axelrod, Claudio Cioffi-Revilla, and the editor and the anonymous reviewers of this journal for excellent comments. Laszlo Gulyas helped me reimplement the model in Java and Repast. Nevertheless, I bear the full responsibility for any inaccuracies and omissions. Since Richardson’s (1948; 1960) pioneering statistical work, we know that casualty levels of wars are power-law distributed. As with earthquakes, there are many events with few casualties, fewer large ones, and a very small number of huge disasters. More precisely, power laws tell us that the size of an event is inversely proportional to its frequency. In other words,

I hereby declare that I am the sole author of this thesis. The is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. ii This thesis undertakes an empirical study of software evolution by analyzing open source software (OSS) systems. The main purpose is to aid in understanding OSS evolution. The work centers on collecting large quantities of structural data cost-effectively and analyzing such data to understand software evolution dynamics (the mechanisms and causes of change and growth). We propose a multipurpose systematic approach to extracting program facts (e.g., func-tion calls). This approach is supported by a suite of C and C++ program extractors, which cover different steps in the program build process and handle both source and binary code. We present several heuristics to link facts extracted from individual files into a combined system model of reasonable accuracy. We extract historical sequences of system models to aid software evolution analysis. We propose that software evolution can be viewed as Punctuated Equilibrium (i.e., long periods of small changes interrupted occasionally by large avalanche changes). We develop two approaches to study such dynamical behavior. One approach uses the evolution spec-trograph to visualize file level changes to the implemented system structure. The other ap-proach relies on automated software clustering techniques to recover system design changes. We discuss lessons learned from using these approaches. We present a new perspective on software evolution dynamics. From this perspective, an evolving software system responds to external events (e.g., new functional requirements) according to Self-Organized Criticality (SOC). The SOC dynamics is characterized by the following: (1) the probability distribution of change sizes is a power law; and (2) the time series of change exhibits long range correlations with power law behavior. We present em-pirical evidence that SOC occurs in open source software systems. iii

Abstract. I discuss the view of communication networks as self-organised critical systems, the mathematical models that may be needed to describe the emergent properties of such networks, and how certain security hygiene schemes may push a network into a super-critical state, potentially leading to large scale security disasters. 1

Motivated by the results of recent laboratory experiments (Yoshida et al. Nature, 424, 303-306 (2003)) as well as many earlier field observations that evolutionary changes can take place in ecosystems over relatively short ecological time scales, several “unified ” mathematical models of evolutionary ecology have been developed over the last few years with the aim of describing the statistical properties of data related to the evolution of ecosystems. Moreover, because of the availability of sufficiently fast computers, it has become possible to carry out detailed computer simulations of these models. For the sake of completeness and to put these recent developments in the proper perspective, we begin with a brief summary of some older models of ecological phenomena and evolutionary processes. However, the main aim of this article is to review critically these “unified ” models, particularly those published in the physics literature, in simple language that makes the new theories accessible to wider audience.

ABSTRACT The power spectrum of local field potentials (LFPs) has been reported to scale as the inverse of the frequency, but the origin of this 1/f noise is at present unclear. Macroscopic measurements in cortical tissue demonstrated that electric conductivity (as well as permittivity) is frequency-dependent, while other measurements failed to evidence any dependence on frequency. In this article, we propose a model of the genesis of LFPs that accounts for the above data and contradictions. Starting from first principles (Maxwell equations), we introduce a macroscopic formalism in which macroscopic measurements are naturally incorporated, and also examine different physical causes for the frequency dependence. We suggest that ionic diffusion primes over electric field effects, and is responsible for the frequency dependence. This explains the contradictory observations, and also reproduces the 1/f power spectral structure of LFPs, as well as more complex frequency scaling. Finally, we suggest a measurement method to reveal the frequency dependence of current propagation in biological tissue, and which could be used to directly test the predictions of this formalism.