Validating Agent Based Social Systems Models

Validating social systems is not a trivial task. The paper outlines some of our past efforts in validating models of social systems with cognitively detailed agents. It also presents some of the challenges faced by us. A social system built primarily of cognitively detailed agents can provide multiple levels of correspondence, both at observable and abstract aggregated levels. Such a system can also pose several challenges including large feature spaces, issues in information elicitation with database, experts and news feeds, counterfactuals, fragmented theoretical base, and limited funding for validation. Our own approach to validity assessment is to consider the entire life cycle and assess the validity under four broad dimensions of methodological validity, internal validity, external validity and qualitative, causal and narrative validity. In the past, we have employed a triangulation of multiple validation techniques, including face validation as well as formal validation tests including correspondence testing

Simulating Shopper Behavior using Fuzzy Logic in Shopping Center Simulation

To simulate real-world phenomena, a computer tool can be used to run a simulation and provide a detailed report. By using a computer-aided simulation tool, we can retrieve information relevant to the simulated subject in a relatively short time. This study is an extended and complete version of an initial research done by Christian and Hansun and presents a prototype of a multi-agent shopping center simulation tool along with a fuzzy logic algorithm implemented in the system. Shopping centers and all their components are represented in a simulated 3D environment. The simulation tool was created using the Unity3D engine to build the 3D environment and to run the simulation. To model and simulate the behavior of agents inside the simulation, a fuzzy logic algorithm that uses the agents' basic knowledge as input was built to determine the agents' behavior inside the system and to simulate human behaviors as realistically as possible

Human behaviour modelling in complex socio-technical systems : an agent based approach

For many years we have been striving to understand human behaviour and our interactions with our socio-technological environment. By advancing our knowledge in this area, we have helped the design of new or improved work processes and technologies. Historically, much of the work in analysing social interactions has been conducted within the social sciences. However, computer simulation has brought an extra tool in trying to understand and model human behaviours. Using an agent based approach this presentation describes my work in constructing computational models of human behaviour for informing design through simulation. With examples from projects in two main application areas of crisis and emergency management, and energy management I describe how my work addresses some main issues in agent based social simulation. The first concerns the process by which we develop these models. The second lies in the nature of socio-technical systems. Human societies are a perfect example of a complex system exhibiting characteristics of self-organisation, adaptability and showing emergent phenomena such as cooperation and robustness. I describe how complex systems theory may be applied to improve our understanding of socio-technical systems, and how our micro level interactions lead to emergent mutual awareness for problem-solving. From agent based simulation systems I show how context awareness may be modelled. Looking forward to the future, I discuss how the increasing prevalence of artificial agents in our society will cause us to re-examine the new types of interactions and cooperative behaviours that will emerge.Depuis de nombreuses années, nous nous sommes efforcés de comprendre le comportement humain et nos interactions avec l'environnement sociotechnique. Grâce à l'avancée de nos connaissances dans ce domaine, nous avons contribué à la conception de technologies et de processus de travail nouveaux ou améliorés. Historiquement, une part importante du travail d'analyse des interactions sociales fut entreprise au sein des sciences sociales. Cependant, la simulation informatique a apporté un nouvel outil pour tenter de comprendre et de modéliser les comportements humains. En utilisant une approche à base d'agents, cette présentation décrit mon travail sur la construction de modèles informatiques du comportement humain pour guider la conception par la simulation. A l'aide d'exemples issus de projets des deux domaines d'application que sont la gestion des crises et de l'urgence et la gestion de l'énergie, je décris comment mon travail aborde certains problèmes centraux à la simulation sociale à base d'agents. Le premier concerne le processus par lequel nous développons ces modèles. Le second problème provient de la nature des systèmes sociotechniques. Les sociétés humaines constituent un exemple parfait de système complexe possédant des caractéristiques d'auto-organisation et d'adaptabilité, et affichant des phénomènes émergents tels que la coopération et la robustesse. Je décris comment la théorie des systèmes complexes peut être appliquée pour améliorer notre compréhension des systèmes sociotechniques, et comment nos interactions au niveau microscopique mènent à l'émergence d'une conscience mutuelle pour la résolution de problèmes. A partir de systèmes de simulation à base d'agents, je montre comment la conscience du contexte peut être modélisée. En terme de perspectives, j'expliquerai comment la hausse de la prévalence des agents artificiels dans notre société nous forcera à considérer de nouveaux types d'interactions et de comportements coopératifs

Holistically Evaluating Agent Based Social System Models

The philosophical perspectives on model evaluation can be broadly classified into reductionist/logical positivist and relativist/holistic. In this paper, we outline some of our past efforts in, and challenges faced during, evaluating models of social systems with cognitively detailed agents. Owing to richness in the model, we argue that the holistic approach and consequent continuous improvement are essential to evaluating complex social system models such as these. A social system built primarily of cognitively detailed agents can provide multiple levels of correspondence, both at observable and abstract aggregated levels. Such a system can also pose several challenges, including large feature spaces, issues in information elicitation with database, experts and news feeds, counterfactuals, fragmented theoretical base, and limited funding for validation. We subscribe to the view that no model can faithfully represent reality, but detailed, descriptive models are useful in learning about the system and bringing about a qualitative jump in understanding of the system it attempts to model – provided they are properly validated. Our own approach to model evaluation is to consider the entire life cycle and assess the validity under two broad dimensions of (1) internally focused validity/quality achieved through structural, methodological, and ontological evaluations; and (2) external validity consisting of micro validity, macro validity, and qualitative, causal and narrative validity. In this paper, we also elaborate on selected validation techniques that we have employed in the past. We recommend a triangulation of multiple validation techniques, including methodological soundness, qualitative validation techniques, such as face validation by experts and narrative validation, and formal validation tests, including correspondence testing

Modeling computational dynamics of job interview candidate's mental states using cognitive agent based approach

Support for job interview is a domain that can benefit from the research on human-aware AI systems. A developed cognitive model provides the awareness of interviewee behaviours as a mechanism for intelligent support processes. The interplaying constructs of self-efficacy, motivation and anxiety has been hypothesized to define the mental states of an interviewee. However, these constructs have not been integrated, formalized and evaluated for their dynamic intricacies in previous studies hence cannot be implemented as the reasoning component in human-aware system. This study has developed a cognitive agent model as a basic intelligent mechanism for interview coaching systems. The model integrates three constructs; self-efficacy, motivation and anxiety. Each of the constructs is formalized as an entity agent model and then integrated. Design Science Research Processes framework and Agent Based Modelling methodology were used to conduct this study. Factors interaction and overlapping relationship approach was adopted to integrate the proposed constructs. The model is formalized using Ordinary Differential Equation technique and later being simulated. Generated cases were verified with stability analysis and automatic logical verifications techniques. For model validation, 36 undergraduate students were studied in a mock interview experiment. The results generated from the model simulation were then compared against human experiment. The evaluation was based on a statistical technique namely Hotelling’s T2. The simulation results have confirmed a number of patterns identified in the domain literature. The behavioural patterns of the agent models conform to the expected behavioural dynamics of candidate in interview situation. Results from the validation showed that there is no significant difference (i.e. ρ values: anxiety = 0.391, self-efficacy = 0.128 and motivation = 0.466) between the simulation and human experiments. Theoretically, by integration of the three constructs, the model could better represent the mental state of candidates in interviews. In general, by formalizing the model, it can define the dynamic properties in details. The integrated cognitive model serves as a platform for designing a human-aware system that understands the behavioural intricacies of the user during job interview sessions

Studying coupled human and natural systems from a decentralized perspective: the case of agent-based and decentralized modeling

The science of coupled human and natural systems deals with the interactions between humans and their environment. This science focuses on the complex dynamics and patterns that emerge through these interactions. One of the most insightful ways to study coupled human and natural systems consists in developing models to reproduce the patterns seen in these systems. Models of coupled human and natural systems are particular in the sense that they require the integration of knowledge from various and differing fields such as economics, social sciences, ecology, hydrology, biology, climate sciences and many others. In this thesis, we claim that most coupled human and natural systems are decentralized and would better be modeled from a decentralized perspective. Agent-based models, especially can be very useful to model human systems. A review of the literature shows that agent-based modeling is a commonly used tool in all the fields related to coupled human and natural systems such as socio-ecology – or social and ecological systems, hydro-economic systems, socio-hydrology or integrated environmental modeling. While agent-based models present a lot of challenges, they appear as promising tools for the representation of humans in models of coupled human and natural systems. Using an agent-based model of farmers’ decision-making on irrigation, coupled with a model of groundwater flow and aquifer/stream interactions, we studied the role of individuals in a coupled agricultural and hydrologic system. The model was designed to simulate the interactions between farmers pumping groundwater to irrigate their corn fields and the water levels within a portion of the aquifer below the Republican River Basin in the High Plains region in Nebraska. A set of simulations show that incorporating behavioral heterogeneity of individuals in the model leads to the formation of spatial and temporal patterns. In other words, some of the patterns found in the real system could be partially explained by behavioral heterogeneity of farmers. Additionally, we find that model results are more accurate when accounting for individual heterogeneity. Including individuals in the model also helps understand how these individuals are impacted by system dynamics such as new policies or environmental change. This can prove useful for policy making when knowing the differences between individuals can help devise better policies. The challenge in modeling individuals and their behavior is to decide how complex these models should be. We suggest that individual behavior should be considered as another source of uncertainty rather than a source of unnecessary complexity

A Continuous-Time Microsimulation and First Steps Towards a Multi-Level Approach in Demography

Microsimulation is a methodology that closely mimics life-course dynamics. In this thesis, we describe the development of the demographic microsimulation with a continuous time scale that we have realized in the context of the project MicMac - Bridging the micro-macro gap in population forecasting. Furthermore, we detail extensions that we have added to the initial version of the MicMac microsimulation.Mikrosimulation ist eine Prognosetechnik, die sich hervorragend eignet, um Bevölkerungsdynamik realitätsnah abzubilden. In dieser Dissertation beschreiben wir die Entwicklung einer demografischen Mikrosimulation, die wir im Rahmen des Projektes MicMac - Bridging the micro-macro gap in population forecasting erstellt haben. Zudem erläutern wir Erweiterungen, die wir an der ursprünglichen MicMac- Mikrosimulation vorgenommen haben