William G. Kennedy

Modelling Human Behaviour in Agent-Based Models

The modelling of human behaviour is not at all obvious. First, humans are not random. Second, humans are diverse in their knowledge and abilities. Third, besides being controlled by rational decision-making, human behaviour is also emotional. This chapter attempts to present principles driving human behaviour and reviews current approaches to modelling human behaviour.

“Like-Me” Simulation as an Effective and Cognitively Plausible Basis for Social Robotics

We present a successful design approach for social robotics based on a computational cognitive architecture and mental simulation. We discuss an approach to a Theory of Mind known as a “like-me” simulation in which the agent uses its own knowledge and capabilities as a model of another agent to predict that agent’s actions. We present three examples of a “like-me” mental simulation in a social context implemented in the embodied version of the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture, ACT-R/E (for ACT-R Embodied). Our examples show the efficacy of a simulation approach in modeling perspective taking (identifying another’s left or right hand), teamwork (simulating a teammate for better team performance), and dominant-submissive social behavior (primate social experiments). We conclude with a discussion of the cognitive plausibility of this approach and our conclusions.

Long-term symbolic learning

What are the characteristics of long-term learning? We investigated the characteristics of long-term, symbolic learning using the Soar and ACT-R cognitive architectures running cognitive models of two simple tasks. Long sequences of problems were run collecting data to answer fundamental questions about long-term, symbolic learning. We examined whether symbolic learning continues indefinitely, how the learned knowledge is used, and whether computational performance degrades over the long term. We report three findings. First, in both systems, symbolic learning eventually stopped. Second, learned knowledge was used differently in different stages but the resulting production knowledge was used uniformly. Finally, both Soar and ACT-R do eventually suffer from degraded computational performance with long-term continuous learning. We also discuss ACT-R implementation and theoretic causes of ACT-Rs computational performance problems and settings that appear to avoid the performance problems in ACT-R.

Towards Validating a Model of Households and Societies in East Africa

One of the major challenges of social simulations is the validation of the models. When modeling societies, where experimentation is not practical or ethical, validation of models is inherently difficult. However, one of the significant strengths of the agent-based modeling (ABM) approach is that it begins with the implementation of a theory of behavior for relatively low-level agents and then produces high-level behaviors emerging from the low-level theory’s implementation. Our ABM model of societies is based on modeling the decision making of rural households in a 1,600 km (1,000 mile) square around Lake Victoria in East Africa. We report on the first validation of our model of households making their living on a daily basis by comparing resulting activities against societal data collected by anthropologists.

Behavioral representation in modeling and simulation: Introduction to CMOT special issue--BRiMS 2009

1 June 2011 The BRiMS Society and Conference (Behavioral Representation in Modeling and Simulation (BRiMS, brimsconference.org) promote cross-disciplinary communication for basic and applied scientific research in the realm of modeling and simulation of human behavior, with a particular emphasis on defense government-related tasks and behavior. Thus, the BRiMS conference brings together scientists, engineers, practitioners, and application users to discuss modeling behavior ranging from that of individuals to the behavior of whole societies, their interactions, and their implications. For a few days each year, we get to meet to share ideas and experiences, identify gaps in current capabilities, discuss new directions, highlight promising technologies, and showcase applications. This special issue is similar to our previous special issue (Kennedy, Ritter, & Best, 2010) in that it includes four papers based on the award winning conference papers of the 2010 annual conference, reviewed and extended to journal article length. The papers include a new model integrating top-down and bottom-up factors affecting visual target acquisition (Jungkunz & Darken, 2011), the application of a statistical methodology to modeling psychological and cognitive impacts of protective clothing (Mueller et al., 2011), the use of fuzzy cognitive mapping techniques to model situation awareness (Jones et al., 2011), and the challenge of exploration and optimization of cognitive models (Moore, 2011). Overall, they represent how the 2010 conference addressed modeling from small-scale models, for example, predicting eye movements, to large-scale parameter exploration using high-performance computing facilities. At the small-scale end of the range of these papers, Patrick Jungkunz and Christian Darken present models of eye movements during target acquisition in military simulations. They found that a relevance map performed better than a salience map and that scene locations that are semantically relevant predict human eye fixations better than just visual salience. However, the combined approach was not statistically better than the relevance map alone. Their work identified semantically relevant scene locations as the most significant factor in predicting eye fixations and they developed a novel method that supports direct extraction of that information directly from the simulation environment. This work is important for simulations because they too often assume that models can see everything (Ritter, Baxter, Jones, & Young, 2000). Not seeing everything or seeing things that are trying not to be seen is important in adversarial simulations (Best & Gerhart, 2011). The second paper concerns the predicting the impact of the psychological and cognitive stressors on performance. Shane Mueller, Benjamin Simpkins, George Anno, Corey Fallon, Gene McCellan, and Owen Price of Applied Research Associates, report on the

How Long Is a Moment: The Perception and Reality of Task-Related Absences

We have investigated actual and perceived human performance associated with a simple task involving walking and applied the developed knowledge to a human-robot interaction. Based on experiments involving walking at a “purposeful and comfortable” pace, parameters were determined for a trapezoidal model of walking: starting from standing still, accelerating to a constant pace, walking at a constant pace, and decelerating to a stop. We also collected data on humans’ evaluation of the accomplishment of a simple task involving walking: determining the transitions from having taken too short a period of time to an appropriate time and from having taken an appropriate time to having taken too long. People were found to be accurate in estimating the task duration for short tasks, but to underestimate the duration of longer tasks. This information was applied to a human-robot interaction involving a human leaving for a “moment” and the robot knows how long the task should take and how time is evaluated by a human.

Multi-scale Resolution of Cognitive Architectures: A Paradigm for Simulating Minds and Society

We put forth a thesis, the Resolution Thesis, that suggests that cognitive science and generative social science are interdependent and should thus be mutually informative. The thesis invokes a paradigm, the reciprocal constraints paradigm, that was designed to leverage the interdependence between the social and cognitive levels of scale for the purpose of building cognitive and social simulations with better resolution. In addition to explaining our thesis, we provide the current research context, a set of issues with the thesis and some parting thoughts to provoke discussion. We see this work as an initial step to motivate both social and cognitive sciences in a new direction, one that represents some unity of purpose and interdependence of theory and methods.

Generation of Realistic Mega-City Populations and Social Networks for Agent-Based Modeling

Agent-based modeling is a means for researchers to conduct large-scale computer experiments on synthetic human populations and study their behaviors under different conditions. These models have been applied to questions regarding disease spread in epidemiology, terrorist and criminal activity in sociology, and traffic and commuting patterns in urban studies. However, developing realistic control populations remains a key challenge for the research and experimentation. Modelers must balance the need for representative, heterogeneous populations with the computational costs of developing large population sets. Increasingly these models also need to include the social network relationships within populations that influence social interactions and behavioral patterns. To address this we used a mixed method of iterative proportional fitting and network generation to build a synthesized subset population of the New York megacity and region. Our approach demonstrates how a robust population and social network relevant to specific human behavior can be synthesized for agent-based models.

Exploratory Models of Trust with Empirically-Inferred Decision Trees

What is the relationship between an individual’s values and their propensity to trust other people? To explore this question, we built decision trees on the microdata provided by the World Value’s Survey. Our findings confirm the extant literature while also hinting at cultural heterogeneity. We propose that studying nationally-specific decision trees based on survey data allows for easy-to-intuit representations of complex social problems. Moreover, for the sake of pragmatism, decision trees developed in this manner offer researchers a good tool in terms of cost-to-benefits.