In the literature on reasoning on the basis of evidence, two traditions exist: one argument-based, and one based on narratives. Recently, we have proposed a hybrid perspective in which argumentation and narratives are combined. This formalized hybrid theory has been tested in a sense-making software prototype for criminal investigators and decision makers. In the present paper, we elaborate on the role of commonsense knowledge. We argue that two kinds of knowledge are essential: argumentation schemes and story schemes. We discuss some of the research issues that need to be addressed.

In this paper, we explore ideas regarding a formal logical model which allows for the use of stories to persuade autonomous software agents to take a particular course of action. This model will show how typical stories – sequences of events that form a meaningful whole – can be used to set an example for an agent and how the agent might adapt his own values and choices according to the values and choices made by the characters in the story.

Narratives are ubiquitous in human experience. We use them - What comprises the set of possible narrative arcs? Is there to educate, communicate, convince, explain, and entertain. How many possible story lines are there? Is As far as we know, every society in the world has narratives, there a recipe (à la Joseph Campbell or Vladimir Propp) which suggests they are rooted in our psychology and serve for generating narratives? an important cognitive function: that narratives do something - What are the appropriate representations of narrative?

Using the well-known Artificial Anasazi simulation for a case study, we investigate the use of genetic algorithms (GAs) for performing two common tasks related to robustness checking of agent-based models: parameter calibration and sensitivity analysis. In the calibration task, we demonstrate that a GA approach is able to find parameters that are equally good or better at minimizing error versus historical data, compared to a previous factorial grid-based approach. The GA approach also allows us to explore a wider range of parameters and parameter settings. Previous univariate sensitivity analysis on the Artificial Anasazi model did not consider potentially complex/nonlinear interactions between parameters. With the GA-based approach, we perform multivariate sensitivity analysis to discover how greatly the model can diverge from historical data, while the parameters are constrained within a close range of previously calibrated values. We show that by varying multiple parameters within a 10% range, the model can produce dramatically and qualitatively different results, and further demonstrate the utility of sensitivity analysis for model testing, by the discovery of a small coding error. Through this case study, we discuss some of the issues that can arise with calibration and sensitivity analysis of agent-based models.

Theory suggests that biological robustness allows for the maintenance of fitness in the face of mutational change, and to the extent that this mutational change translates to heritable phenotypic change, that biological robustness allows for evolvability. However, empirical demonstrations that robustness promotes evolvability remain scant. This is in part due to the difficulty of defining and measuring both evolvability and robustness in real biological systems. Here we test whether protein structural robustness is associated with the extent of adaptive change a protein experiences. We find this to be the case for two forms of protein robustness—designability and modularity, which we measure via contact density and helix/sheet density, respectively. We interpret this association to be primarily the result of reduced constraints on amino acid substitutions in highly designable and/or modular proteins, resulting in less antagonistic pleiotropy and faster adaptation through natural selection.

MAMmodels are inherent in CAS as a holistic System. Multi-agent modeling is based on "down-up" Many surprising properties of the Economic Systems (such methodology, starting from the interaction of a multitude as sudden crises, jumps of macro-indices, catastrophe-like of "agents" to revealing the emergent properties of the changes of the system) can be understood deeper on the integral system.

The coagulation system (CS) is a complex, inter-connected biological system with major physiological and pathological roles. Adaptive mechanisms such as ubiquitous feedback and feedforward loops create non-linear relationships among its individual components and render the study of this biology at a molecular and cellular level nearly impossible. Computational modeling aims to overcome limitations of current analytical methods through in silico simulation of these complex interplays. We present herein an Agent Based Modeling and Simulation (ABMS) approach for simulating these complex interactions. Our ABMS approach utilizes a subset of 48 rules to define the interactions among 24 enzymes and factors of the CS. These rules simulate the interaction of each “agent”, such as substrates, enzymes, and cofactors, on a two-dimensional grid of ~3,000 cells and ~500,000 agents. Our ABMS method demonstrates the robustness of the physiologic CS system over large ranges of tissue factor (TF) concentrations. The system also demonstrates fragility as complete coagulation occurs at sufficiently high concentrations of TF. Removal of individual coagulation inhibitors from the physiologic system results in system fragility at relatively lower TF concentrations. The complete removal of coagulation inhibitors leads to a system that is incapable of controlling coagulation at all TF concentrations. The synergistic effects of the inhibitory pathways create an intricate regulatory mechanism that allows sufficient clot formation while preventing system wide activation of the CS; a robust system emerges.

Attitudes play a significant role in determining how individuals process information and behave. In this paper we have developed a new computational model of population wide attitude change that captures the social level: how individuals interact and communicate information, and the cognitive level: how attitudes and concept interact with each other. The model captures the cognitive aspect by representing each individuals as a parallel constraint satisfaction network. The dynamics of this model are explored through a simple attitude change experiment where we vary the social network and distribution of attitudes in a population.

In this paper we make a simple theoretical point using a practical issue as an example. The simple theoretical point is that robustness is not 'all or nothing': in asking whether a system is robust one has to ask 'robust with respect to what property?' and 'robust over what set of changes in the system?' The practical issue used to illustrate the point is an examination of degrees of linkage between sub-networks and a pointed contrast in robustness and fragility between the dynamics of (1) contact infection and (2) information transfer or belief change. Time to infection across linked sub-networks, it turns out, is fairly robust with regard to the degree of linkage between them. Time to infection is fragile and sensitive, however, with regard to the type of sub-network involved: total, ring, small world, random, or scale-free. Aspects of robustness and fragility are reversed where it is belief updating with reinforcement rather than infection that is at issue. In information dynamics, the pattern of time to consensus is robust across changes in network type but remarkably fragile with respect to degree of linkage between sub-networks. These results have important implications for public health interventions in realistic social networks, particularly with an eye to ethnic and socio-economic sub-communities, and in social networks with sub-communities changing in structure or linkage.

Here we present a network model in which self-sustaining recurrent activation emerges from simple cascades of activation. It is demonstrated that the ability to support such self-sustaining activation in our model depends on network connectivity as well as the ability to grow new links over time. Additionally, we explore how the probability of emergence of self-sustaining activity can be modulated by changing various network parameters and suggest potential applications of our findings.