Wheat Resources Inc.
Prior to joining academia full-time, I worked with more than 140 clients in both the public and private sectors. In the early years in Texas, the clients were mostly in the energy industry, and the work focused on regulatory economics. After relocating to Virginia under the corporate banner of Wheat Resources, I diversified my client list and became engaged in strategic planning, workforce training and economic development projects, economic monitoring and forecasting, and the use of system dynamics modeling to improve decision making and policy design.
Policy analysis and design always proceed on the basis of some model of the way the real world generates policy problems. Typically, such models are structures within human memory--mental models--that represent the cumulative experience and insight of those living with or studying the problem. Policy options are then evaluated by mental simulations of expected cause-and-effect relationships.
Persistent policy issues, however, typically arise in systems containing complex information feedback structures and nonlinear relationships, and it is extremely difficult to mentally simulate expected effects. Moreover, when diverse members of a team of analysts engage their individual mental models, the likelihood that they will share the same image of an unfolding process is slim. Any policy design compromise in that context probably depends more on the relative persuasive skills of the analysts than on the relative quality of their mental models.
Many policy issues can be modeled formally, using simulation methods. Even when an issue is seemingly intractable and a useful simulation model is beyond grasp, the process of debating alternative dynamic hypotheses in feedback terms can be illuminating. Often, a shared mental model is an important objective within a group of analysts and managers. In that case, a well-developed discipline for thinking systemically can be valuable even to those who do not engage in formal computer modeling.