My main interests are system dynamics and multi agent simulation used for support of business and marketing decisions (e.g. modeling of consumer markets) and in business education (e.g. development of open source business simulators). Amongst my other interests are applied marketing research, relationships between academia and industry, financial literacy, mind and concept mapping.

My research interests include policy informatics and decision making, modeling in policy analysis and management decisions, public health management and policy, and the role of public value in policy development. I am particularly interested in less mainstream approaches to modeling that account for learning, feedback, and other systems dynamics. I include Bayesian inference, agent-based models, and behavioral assumptions in both my research and teaching.
In my dissertation research, I conceptualize state Medicaid programs as complex adaptive systems characterized by diverse actors, behaviors, relationships, and objectives. These systems reproduce themselves through both strategic and emergent mechanisms of program management. I focus on the mechanism by which citizens are sorted into or out of the system: program enrollment. Using Bayesian regression and agent-based models, I explore the role of administrative practices (such as presumptive eligibility and longer continuous eligibility periods) in increasing enrollment of eligible citizens into Medicaid programs.

The particular focus of the research is the development of a spatial and Agent-Based Model to capture the motivations underlying the decision making of the various actors towards the investments in the quality of land and institutions, or other aspects of land use change. Inductive models (GIS and statistical based) can extrapolate existing land use patterns in time but cannot include actors decisions, learning and responses to new phenomena, e.g. new crops or soil conservation techniques. Therefore, more deductive (‘theory-driven’) approaches need to be used to complement the inductive (‘data-driven’) methods for a full grip on transition processes. Agent-Based Modeling is suitable for this work, in view of the number and types of actors (farmer, sedentary and transhumant herders, gender, ethnicity, wealth, local and supra-local) involved in land use and management. NetLogo framework could be use to facilitate modeling because it portray some desirable characteristics (agent based and spatially explicit). The model develop should provide social and anthropological insights in how farmers work and learn.

Behavioural ecology and modelling of ant behaviour, with an emphasis on understanding how individual-level complexity affects collective decision-making

Agent-based modelling of migration decision-making under changing environmental conditions.

I have been studying (1) applied discrete choice modelling, (2) consumer choices of seafood, (3) international seafood trade, (4) marine habitat and fishery management, (5) China’s international relation, (6) environment and health, and (7) experimental auctions.

I’m starting to learn ABM and hope to apply the method into my research.

1. Dynamics of socio-ecological resource use systems
   • Pasture use in dryland grazing systems under change, effects of new technologies and policy instruments, emergence of polarization between pastoralists (e.g. in terms of livestock numbers).
   • Thresholds of disaster management performance under change, loss of manpower, the role of information as critical resource.
2. Human decision making in agent-based models.
3. Remote sensing and GIS.

I am interested in the study of small-group decision-making using agent-based simulation of models grounded in sociological social psychology. I am also interested in a particular kind of small-group decision-making: peer review.