Modeling strategic interactions

Advanced System Analysis (ASA) Program researchers develop game-theoretic methods to better understand the strategic interactions between multiple agents and model the behavior of countries involved in international environmental agreements to find ways of inducing cooperation.

© Rawpixelimages | Dreamstime

© Rawpixelimages | Dreamstime

Game theory is commonly used to describe strategic interactions between multiple agents in situations when decisions of one agent affect the outcomes of decisions of others. Environmental issues are examples of such situations: agents—countries, industries, and individuals—make decisions aiming to optimize their interests, which often leads to overexploitation of the common resource.

Analyzing the role of economic instruments in environmental regulation is essential for designing policy recommendations. ASA researchers demonstrated that because of the market volatility, the traditional deterministic emission-trading scheme will not achieve its declared goal of minimizing the actual cost of emissions reductions; instead, they suggested a stochastic model of the market-based emission abatement and trade [1][2]. This model generates robust emission policies under environmental safety constraints, asymmetric information, and other anthropogenic and natural uncertainties. Explicit treatment of uncertainties provides incentives for reducing them before trading. ASA researchers also worked on the catastrophic risks caused by natural events or terrorist threats, using the theory of controlled Markov fields to demonstrate that these problems can be reduced to finite-dimensional stochastic programming problems and solved by the stochastic quasi-gradient method [3].

ASA research has also investigated cases where it is the international regulator who has selfish interests. In cases where countries produce both global and local environmental pollution and results showed that emission trading is welfare diminishing, because it grants less (more) permits to countries with relatively clean (dirty) localized technology [4]. In a situation where natural resources are used both in production or conservation and minimum standards for conservation are used, conservation subsidies are welfare decreasing, involving excessive conservation [5]. This analysis suggested policy implications for the EU project NATURA 2000, for example, which supported the network of core habitat for rare and threatened species. According to the research, “co-financing’’ these sites leads to underestimation of the social opportunity costs and results in creating too many of them [5].

The incentives for agents to invest in costly protection against cascading failures in a networked system is a key factor in reducing systemic risk, and ASA research has characterized the equilibrium based on an agent's failure probability and derived conditions under which equilibrium strategies are monotone in how connected an agent is on network [6].

The behavior of individuals, business, and even countries, is often only based on “bounded rationality.” ASA researchers have investigated how the numerous models created to investigate this problem differ both from observed behavior and from each other. Analyzing a class of bounded rationality models, they were able to show precisely why observed behavior was not predicted by these models, and provided the mathematically necessary conditions for future models to more accurately predict this behavior [7]. This research relies heavily on the theoretical work in [8], which showed that interactions between agents could be uniquely divided into those that encourage individuals to seek personally preferred payoffs and those that required cooperation among players.


[1] Ermoliev Y, Ermolieva T, Jonas M, Obersteiner M, Wagner F & Winiwarter W (2015). Integrated model for robust emission trading under uncertainties: cost-effectiveness and environmental safety. Technological Forecasting and Social Change 98: 234-244.

[2] Ermolieva T, Ermoliev Y, Jonas M, Obersteiner M, Wagner F & Winiwarter W (2015). Uncertainty, cost-effectiveness and environmental safety of robust carbon trading: integrated approach. In: Greenhouse Gas Inventories: Expanding Our Uncertainty Perspective, eds. Ometto JP, Bun R, Jonas M & Nahorski Z. pp 183-196. Dordrecht, Netherlands: Springer.

[3] Haivoronskyy OO, Ermoliev Y, Knopov P& Norkin V (2015). Mathematical modeling of distributed catastrophic and terrorist risks. Cybernetics and Systems Analysis 51(1): 85-95.

[4] Palokangas TK (2015a). Emission Permit Management with a Self-Interested Regulator. Helsinki Center of Economic Research discussion papers 390.

[5] Palokangas TK (2015b). Regulation versus Subsidies in Conservation. Helsinki Center of Economic Research discussion papers 388.

[6] Leduc MV & Momot R (2015). Strategic Investment in Protection in Networked Systems, in: Web and Internet Economics, 11th International Conference, WINE 2015. Amsterdam, The Netherlands, p. 434. ISBN 978-3-662-48994-9

[7] Jessie DT & Saari DG (2015a). From the Luce Choice Axiom to the Quantal Response Equilibrium. Journal of Mathematical Psychology.

[8] Jessie DT & Saari DG (2015b) Strategic and behavioral decomposition of games. IIASA Interim Report IR-15-001.


Ecosystem Services and Management Program (ESM), IIASA

Mitigation of Air Pollution and Greenhouse Gases Program (MAG), IIASA

Glushkov Institute of Cybernetics, National Academy of Sciences of Ukraine, Ukraine

University of California, USA

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Last edited: 15 March 2016


Elena Rovenskaya

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Advanced Systems Analysis

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