Computational social science, as with all complex adaptive systems sciences, involves a great amount of uncertainty on several fronts, including intrinsic arbitrariness such as that due to path dependence, disagreement on social theory and how to capture it in software, input data of different credibility that does not exactly match the requirements of software because it was gathered for another purpose, and inexactly matching translations between models that were designed for different purposes than the study at hand. This paper presents a method of formally tracking that uncertainty, keeping the data input parameters proportionate with logical and probabilistic constraints, and capturing proportionate dynamics of the output ordered by the decision points of policy change, for the purpose of risk-based analysis. Once ordered this way, the data can be compared to other data similarly expressed, whether that data is from simulation excursions or from the real world, for objective comparison and distance scoring at the level of dynamic patterns as opposed to single outcome validation. This method enables wargame adjudicators to be run out with data gleaned from the wargame, enables data to be repurposed for both training and testing set, and facilitates objective validation scoring through soft matching. Artificial intelligence tools used in the method include probabilistic ontologies with crisp and Bayesian inference, game trees that are multiplayer non-zero sum and decision point based rather than turn-based, and Markov processes to represent the dynamic data and align the models for objective comparison.

When market works and when it fails has been an issue long pursued by economists. While to an extreme extent the view, as characterized by the “invisible hand” or “market mechanism”, has been so dominant in economics education and public policy debates, it is generally acceptable that markets are not out there and have to designed properly so as to work (McMillan, 2004). The significance of designs has been further illustrated by experimental economics. As opposed to designs, what, however, has been drawn less attention is the role of traders, their characteristics and behavior. To one extreme, one may consider that a good design is so dominant that there leaves little room for individual traders to play a role. The literature inspired by the zero-intelligence agent (Gode and Sunder, 1993) provides a good background of this issue, and many later studies do cast doubt on the sufficiency of this minimal intelligence and propose different versions of additional intelligence.  

The paper describes ongoing integrated research on designing intelligent robots that can assist humans in making a situation assessment during Urban Search & Rescue (USAR) missions. These robots (rover, microcopter) are deployed during the early phases of an emergency response. The aim is to explore those areas of the disaster hotzone which are too dangerous or too difficult for a human to enter at that point. This requires the robots to be "intelligent" in the sense of being capable of various degrees of autonomy in acting and perceiving in the environment. At the same time, their intelligence needs to go beyond mere task-work. Robots and humans are interdependent. Human operators are dependent on these robots to provide information for a situation assessment. And robots are dependent on humans to help them operate (shared control) and perceive (shared assessment) in what are typically highly dynamic, largely unknown environments. Robots and humans need to form a team. The paper describes how various insights from robotics and Artificial Intelligence are combined, to develop new approaches for modeling human robot teaming. These approaches range from new forms of modeling situation awareness (to model distributed acting in dynamic space), human robot interaction (to model communication in teams), flexible planning (to model team coordination and joint action), and cognitive system design (to integrate different forms of functionality in a single system).

In all of these problems, we have limited with researchers in other disciplines, be "on the ground" security resources which prevent full security coverage with domain experts, and examine real-world constraints at all times; instead, limited security resources must be deployed and challenges that cannot be abstracted away. Together as intelligently taking into account differences in priorities an international community of multiagent researchers, we of targets requiring security coverage, the responses of can accomplish more! the adversaries to the security posture and potential uncertainty over the types, capabilities, knowledge and priorities

This would allow teams to have an objective currency for trust transactions. These systems would allow another surface for autonomous Ali, A.S., Rana, O., and Walker, D.W. (2004): "WS-QoC: agents to integrate the social fabric with information Measuring Quality of Service Compliance," International gathered in virtual environments. Further, the system would Conference on Service Oriented Computing (ICSOC04), New increase illumination of dark networks engaged in illicit York, NY. covert activity. Participants would be assigned a score Allbeck, J., and Badler, N. (2002): "Toward Representing Agent similar to FICO scores; when an individual score falls Behaviors Modified by Personality and Emotion," Autonomous noticeably or falls below a threshold, further observation Agents and Multiagent Systems, Bologna, Italy.

In this paper we examine the case of a mobile robot that is part of a human-robot urban search and rescue (USAR) team. During USAR scenarios, we would like the robot to have a geometrical-functional understand- ing of space, using which it can infer where to perform planned tasks in a manner that mimics human behav- ior. We assess the situation awareness of rescue work- ers during a simulated USAR scenario and use this as an empirical basis to build our robot’s spatial model. Based upon this spatial model, we present “functional map- ping” as an approach to identify regions in the USAR environment where planned tasks are likely to be opti- mally achievable. The system is deployed and evaluated in a simulated rescue scenario.

Nexus Network Learner is an intelligent agent based simulation used to study Irregular Warfare (IW) in several major studies at the Department of Defense (DoD). Heterogeneous autonomous agents, each with their own separated inductive learning mechanism, have initial attributes and behaviors in proportion to demographic groups in the simulated population, and learn new behaviors as they serve culturally based goals. Nexus agents create a dynamic role-based network, and learn how to choose partners as well as what behaviors they should have with their network partners. As Nexus agents coevolve, nexus models the emergence of social institutions from individual behaviors, the fundamental social aggregation challenge. Nexus models the formation of learned vicious and virtuous cycles of behavior, some of which have higher average utility for the agents than others, and can be used to test the effects of interventions on the natural motivation-based system. An experiment is presented that uses Nexus to model the vicious cycle of corruption in an African country, from the first Irregular Warfare Analytical baseline at the Office of the Secretary of Defense (Messer 2009).

Stackelberg games have been used in several deployed applications of game theory to make recommendations for allocating limited resources for protecting critical infrastructure. The resource allocation strategies are randomized to prevent a strategic attacker from using surveillance to learn and exploit patterns in the allocation. An important limitation of previous work on security games is that it typically assumes that attackers have perfect surveillance capabilities, and can learn the exact strategy of the defender. We introduce a new model that explicitly models the process of an attacker observing a sequence of resource allocation decisions and updating his beliefs about the defender's strategy. For this model we present computational techniques for updating the attacker's beliefs and computing optimal strategies for both the attacker and defender, given a specific number of observations. We provide multiple formulations for computing the defender's optimal strategy, including non-convex programming and a convex approximation. We also present an approximate method for computing the optimal length of time for the attacker to observe the defender's strategy before attacking. Finally, we present experimental results comparing the efficiency and runtime of our methods.

Natural world interaction (NWI), the pursuit of arbitrary goals in unstructured physical environments, is an excellent motivating problem for the reintegration of artificial intelligence. It is the problem set that humans struggle to solve. At a minimum it entails perception, learning, planning, and control, and can also involve language and social behavior. An agent's fitness in NWI is achieved by being able to perform a wide variety of tasks, rather than being able to excel at one. In an attempt to address NWI, a brain-emulating cognition and control architecture (BECCA) was developed. It uses a combination of feature creation and model-based reinforcement learning to capture structure in the environment in order to maximize reward. BECCA avoids making common assumptions about its world, such as stationarity, determinism, and the Markov assumption. BECCA has been demonstrated performing a set of tasks which is non-trivially broad, including a vision-based robotics task. Current development activity is focused on applying BECCA to the problem of general Search and Retrieve, a representative natural world interaction task.

Traditional social science research has been unable to satisfactorily aggregate individual level data to group, organization and systems levels, making it one of social science’s biggest challenges (Giles, 2011). For game and social theory, we believe that the fault can be attributed to the lack of valid distance measures (e.g., the arbitrary ordering of cooperation and competition precludes a Hilbert space distance metric for the ordering of and gradations between these social behaviors, making game theory normative). Alternatively, we offer a theory of social interdependence with countable mathematics based on bistable or multi-stable perspectives and linear algebra. The evidence that is available is supportive. It indicates that meaning is a one-sided, stable, classical interpretation, not only making the correspondence between beliefs and objective reality in social settings incomplete, raising questioning about static theories from earlier eras (i.e., Axelrod’s evolution of cooperation; Simon’s bounded rationality). The result indicates for open systems (democracies) that interpretations evolve naturally to become orthogonal (Nash equilibria), that orthogonal interpretations generate the information to drive social evolution, but that in closed systems (dictatorships), dependent on the enforcement of social cooperation and the suppression of opposing points of view, evolution slows or stops (e.g., China, Iran or Cuba), causing capital and energy to be wasted, misdirected or misallocated as leaders suppress the interpretations that they alone have the authority to label as unethical, immoral, or irreligious. We conclude that a mathematics based on NE is feasible.