SEGS Mission

The Social-Ecological Gaming and Simulation (SEGS) Lab is a transdisciplinary research lab focused on modeling and simulating Social-Ecological Systems (SESs) from a complex systems perspective.

We draw on a range of data sources, from simple metadata, to "big data" and thick description.

A unique feature of the SEGS lab is its focus on the design and implementation of a variety of experimental games and computational models.

The SEGS lab team aims at forging new partnerships at state, national and international scales with scholars and practitioners working in this fast-growing area of research and practice at the nexus of social and behavioral sciences, and ecology.

SEGS lab research and outreach focuses on the following thematic areas:

1. Social-Ecological System (SES) Simulation:

   The SEGS lab develops novel agent-based models, system dynamic models, numerical simulation models and Bayesian network models to simulate the dynamics of SESs. Some key SESs studied include tropical forest systems, watersheds, airsheds and forest-farm frontiers. Coupled natural-human and socio-technological systems modeling is also envisioned.

2. Social-Ecological Gaming:

   The SEGS lab designs interactive games and brings in subjects to the lab-space to play the games, generate the data and then assimilate the data using SES modeling. Field games and online games are also designed that advance the knowledge base for understanding the dynamics and evolution of SESs.

3. Policy Analytics:

   Computational models of policy analysis and program evaluation are developed in various substantive domains, (such as food policy, environmental policy, transport policy, foreign policy, defense policy, health policy, social policy etc.), at multiple scales of governance to analyze current and past policies and inform the development of future policies based upon the learning gained from computational models.

4. Governance Informatics:

   Complex systems-based analytical tools, such as agent based models, complex system dynamic models, neural networks, genetic algorithms etc, are deployed to simulate and visualize dynamics of governance networks in different policy domains and use the resultant information and knowledge to improve the practice and performance of governance networks.

5. Capacity Building:

   Given the onslaught of information and computational age, government agencies need to be upgraded and brought up-to-date in the usage of new informational and computational tools, real-time decision support systems, and knowledge management. Specific training seminars and certificate programs are being planned for public officials both from developed and developing countries operating in various policy domains.