A System Dynamics GAN Perspective

System dynamics is an experimental design process that can be applied to understand complex social problems and design policies to address them through use of formal models and computer simulation. Developed at MIT over the past half century and now applied globally to address complex problems in business and public policy and organizational design, system dynamics method lends itself easily to a variety of research and design agendas concerning global public policy.

The implementation of system dynamics method to address specific problems involves several carefully designed steps aimed at creating a clear understanding of the problem as well as the possibilities for system improvement. These steps include 1) representation of a pattern of trends portraying the problem, 2) identification of a causal map that qualitatively describes how the problem is created, 3) articulation of the decision relationships underlying the causal map into a computer model and 4) experimentation with the model to learn about the problem and the possible ways to mitigate it.

The modeling process can be as important at the model itself. The process assists people in identifying their assumptions and testing their beliefs and assertions. In this way, it plays an important role in generating dialogue between system participants. Since the modeling can present different insights and points of view in a more objective fashion, it provides a relatively neutral language and framework to help surface more subjective but also critical issues. Hence it is ideal for use in the global public policy context where many actors with different perspectives on issues interact.

As the global economic system becomes highly integrated, concomitant methodological advancements have no doubt also greatly increased our ability to understand the increasingly complex problems of sustainability. However, both formulation of policies for social and economic development and their implementation now involve actors and institutions operating beyond national boundaries, which makes it exceedingly difficult to converge on shared perceptions of both problems and the roles and responsibilities of the various local and global organizations. This has led to much controversy. Table 1 lists the key actors involved in the formulation of global public policy and their performance expectations from the various local and global organizations they are trying to influence.

The key actors participating in any global accords include global agencies like the World Bank, the World Trade Organization, the various developmental agencies of the United Nations and European Union and the various regional strategic and trade alliances. They also include national governments, industry, the public and a relatively new actor - the non-government advocacy organizations often referred to as the civil society. These actors bring different goals and different mental models about the sources of problems to the formulation of national and global policies.

Table 1: GPP actors and their expectations

The various systems whose performance is influenced include the global economy, the national economies, the production units and the natural resource system, albeit with different expectations. Thus, the global agencies would like the global economy to grow but without any structural change. This means that the national economies must maintain their existing position in the global system, while production units compete to deliver a larger output. Of course, the resource system must accommodate the aggregate performance expectations without disrupting the current consumption structure.

The national governments require that the national economies grow, and to accommodate this, the global economy changes in structure. They expect the production units to generate enough tax revenue and foreign exchange so burgeoning national security and defense needs are met and national debts are serviced. They also expect to exploit their natural resources, which includes logging tropical rain forests and burdening pristine resort environments as much as possible to accommodate growth.

The industry would like to have free access to the global markets and also the freedom to move capital and labor resources at will while its proprietary rights to intellectual property are maintained. The industry also expects minimal intervention by national governments and expects that the production units it operates would generate profit and that it is allowed to freely exploit the natural resource system in terms of mining nonrenewable resources and disposing industrial wastes.

The civil society organizations expect all global, national and local production systems to accommodate the various special public agendas they attempt to articulate while the natural resource system is preserved and nurtured. Finally, the public expects that the global economy will create new opportunities for them, the national economy will deliver welfare, the production units would provide meaningful employment and the natural resource system would support an improvement in their standard of living.

Unfortunately, this variety of expectations combined with an even larger variety of perceptions about how the different systems function has led mainly to controversies and anomalies instead of generating any effective policies. In particular, global public policy that mainly concerns trade and environmental agenda has been difficult to formulate and implement due to disagreements among the proponents of free trade, economic efficiency, responsibility for environmental cost and fair trade.

In this environment, GANs can play a much needed functional role by creating consensual knowledge that resolves controversies and converges opinions related to the agendas they articulate, which can be greatly facilitated by attempting to unify knowledge using the experimental available in system dynamics modeling. Since its introduction almost forty years ago, system dynamics modeling has been applied to a variety of pursuits, ranging from advanced research in universities and research organizations, to brainstorming in boardrooms, to classroom learning in pre-college education, to systems thinking for everyday use.

Implemented over the course of a negotiation, system dynamics modeling process is invaluable for creating a shared vision leading towards a resolution that is based on the logic of the problem rather than on the adversarial views articulated during the negotiation. A logic-based mitigation process will indeed elicit greater commitment and cooperation on the part of negotiating parties than an adversarial process based on different mental models of the problem.

System dynamics modeling and its implementation in negotiation and learning contexts are, however, highly specialized skills that require both training for the participants and facilitation of the collective learning process leading to a shared vision. An attempt to carefully articulate problems required for formally modeling them, building system dynamics models around carefully articulated problems and experimenting with the models to design developmental interventions can deliver effective public policy for implementation both at local and global levels. It can also help the actors involved in policy formulation to converge to a shared view of the problems through the learning process involved in problem definition and modeling processes.

System dynamics modeling would also help to design an organization in which a GAN can develop and sustain itself.

As illustrated in the map in Figure 1 below, the growth in GAN membership can be seen as a diffusion process, which is influenced by many tangible and intangible factors that need to be investigated. Such a developmental process can lead to rapid growth followed by an early demise, especially when a network fails to perform. The performance of a GAN may not be related to its size, but on organizational factors that determine its commitment and mentoring and consensus creating roles. Hence developing instruments that sustain the network and its performance is an important task. That can be greatly facilitated by experimenting with a model of the network growth and development process.

A preliminary map of growth and development of a GAN


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