In this paper we describe a complex adaptive systems model of interactions between coupled human and natural system. We use learning classifier systems to create adaptive agents in a simulation of the Maine lobster fishery to explore the relationships among ecological, economic, and social characteristics. Our hypothesis is that the cost of information and learning drives agents' decisions to compete or co-operate and, consequently, the emergence of long-term relationships. Initial results provide tentative support for the hypothesis and the ability of this model to provide insight into the dynamics of individual interactions and the social relationships that emerge from those interactions.

This research aims to define a novel framework to employ engineering and mathematical models to study adaptive dynamics in heterarchial systems. This multi-profile descriptive platform and modeling approach is developed as a composite of conceptual behaviors and structural entity aspects of engineered complex adaptive systems (ECAS). While the US electric power system will be utilized for demonstration and validation, the framework has applicability to the general class of ECASs that are artificially created but highly interactive with natural and behavioral sciences. Conditioned on parameterization of the framework, a theorem will be presented to calibrate current structure and predict future dynamic behaviors of an ECAS. We analyze decentralized heterarchial ECASs to infer emergent behavior of the components, and evolution processes and adaptations of the whole system.

Beyond belief change and meme adoption, both genetics and infection have been spoken of in terms of information transfer. What we examine here, concentrating on the specific case of transfer between sub-networks, are the differences in network dynamics in these cases: the different network dynamics of germs, genes, and memes. Germs and memes, it turns out, exhibit a very different dynamics across networks. For infection, measured in terms of time to total infection, it is network type rather than degree of linkage between sub-networks that is of primary importance. For belief transfer, measured in terms of time to consensus, it is degree of linkage rather than network type that is crucial. Genes model each of these other dynamics in part, but match neither in full. For genetics, like belief transfer and unlike infection, network type makes little difference. Like infection and unlike belief, on the other hand, the dynamics of genetic information transfer within single and between linked networks are much the same. In ways both surprising and intriguing, transfer of genetic information seems to be robust across network differences crucial for the other two.

Skeptics tend not to be the first to jump on the next band- wagon. In quite a few areas of science, simulations and Com- plex Adaptive Systems (CAS) has been the bandwagon in question. This paper intends to reach out to the skeptics and convince them to hop-on; take over the controls and make the wagon do a U-turn and aim for the established scientific theories. The argument is that simulation techniques, such as Agent- Based Modelling (ABM), may possibly be epistemically problematic as one sets out to strongly corroborate theories concerned with our overly complex real world. However, us- ing the same techniques to explore the robustness of (or to falsify) existing abstract and idealised mathematical models will be to be epistemically uncomplicated. This allows us to study the effects of reintroduction of real-world traits, such as autonomy and heterogeneity that was previously sacrificed for mathematical tractability.

Millions of people globally have been forcibly displaced from their homes due to reasons beyond their control such as conflict, political upheaval, and environmental catastrophes. In many cases, these forced migrants seek temporary refuge in camps managed by nongovernmental organizations (NGOs). Although responsibility for refugees’ well-being within camps belongs mainly to the NGOs and host government, the density of the camp population and lack of resources of service providers leads to a high degree of insecurity. Building off successful models of mHealth, or utilizing mobile technologies to address healthcare needs, this paper explores the possibility of using communication technologies to address personal security issues. Using agent based modeling techniques, this paper examines the ways in which information about incidents of violence are communicated through a closed population. In this way, the authors advocate for the use of mobile phones in an mSecurity context that empowers forced migrants to become active members in reducing incidents of violence within refugee and internally displaced persons camps.

Broadly speaking, information has something to do with order or organization within a system of elements. The thermodynamic concept of entropy is also associated with such systems, although in an inverse relationship. When we attempt to put these two apparently coordinated schemas of order and disorder together, all kinds of difficulties arise. I will briefly examine contemporary efforts to unify these two ways of conceiving order and show that they are substantially incompatible. In this process I will draw some distinctions that will lead to a broader reconciliation of the concepts of order and information. I will then attempt to re-evaluate the fundamental models behind these dissonant traditions for formulating order in an attempt to reframe a synthesis of conceptual structures that are mutually reconcilable. I will try to show that such a synthesis can finally make sense of the stubborn inconsistencies that persist in the ways Newtonian dynamics, thermodynamics and biology utilize the implicitly conflicting arrows of time.

Complex adaptive systems (CAS) promise to be useful in modeling and understanding real-world phenomena, but remain difficult to validate and verify. The authors present an adaptive, tool-chain-based approach to continuous validation and verification that allows the subject matter experts (SMEs) and modelers to interact in a useful manner. A CAS simulation of the ICU at the Mayo Clinic is used as a working example to illustrate the method and its benefits.

International networks, although highly efficient, may produce surprising threshold effects that shift costs to geographically distant locations. International utility, transportation, and information networks facilitate the efficient flow of information, energy, goods and people. These networks exhibit a scale-free network structure with a few large “hubs”. Yet their efficiency belies their lack of robustness. Because such networks transcend national boundaries, furthermore, disruptions to the network in one geographic region may have profound economic and national security costs for countries in another region. To illustrate how complex networks may transmit costs among countries, this paper builds an agent-based model (ABM) of the international air transportation system. The ABM employs a genetic algorithm to identify “small” disruptions that produce cascading network failures. The study makes two contributions. First, it demonstrates how some complex networks evolve into network structures that trade off robustness for efficiency. Second, it illustrates how researchers can combine agent-based modeling, evolutionary computation, and network analysis to simulate differing failure modes for global networks. This convergence of simulation methodologies characterizes the emerging field of computational social science.

Simulation techniques have long sustained research in various domains of physical, biological, and social sciences. Currently, humanists are exploring the usefulness of simulations for addressing various research questions. The nature and challenges of this enterprise are presented here in respect to collaborative work, the relation of humanities to the sciences, the transformative nature of digital methods of research within the humanities. This article describes a coordinated attempt to pursue these issues via a Summer Institute funded by the National Endowment for the Humanities, and briefly notes the projects of three of the Institute’s participants. Their work is described in detail elsewhere within this volume.

The paper provides an investigation and identification of the values and assumptions that influence the applications and results derived from the applications of game theoretical models to solve logistical problems. The ultimate goal is to identify and begin to eliminate prejudices and blind spots within the formulation of the game theory constraints and the application itself. We explore the investigation and identification of blind spots through the application of game theoretical models to solve problems concerning resource allocation. In particular, the paper investigates the application of game theoretical models to allocate resources in crisis situations.