Games with Dynamic Externalities and Climate Change

1. Games with Dynamic Externalities and Climate Change Tatsuyoshi Saijo*, Katerina Sherstyuk**, Nori Tarui** and Majah-Leah V. Ravago** *Osaka University and **University of Hawaii at Manoa MSRI Workshop -- May 2009

2. Buildup is slow to reverse itself. Science of Global warming • Human activities (primarily the burning or fossil fuels) intensify the warming effect by releasing GHGinto the atmosphere. Source: http://science.nationalgeographic.com/science/environment/global-warming/gw-overview-interactive.html

3. Nature of the CC problem • Global public good (bad) – total GHG stock is what matters • Huge potential cost and effects worldwide • Unilateral emission reduction favors all countries =>=>Free-rider problem • Irreversibility – GHG accumulate faster and deplete slowly, effect of emission today can be felt into the distant future => =>Dynamic externalities Thus, a social dilemma setting and dynamic externalities are essential features of the problem. Static externalities are less pronounced.

4. Research focus and questions • Games with dynamic externalities • Current action of each player affects not only the players’ payoff this period but also the payoff level of the game that will be played tomorrow. • Inter-generational aspect • Applied toclimate change • The benefits derived by the future generations depend on the stock of GHG buildup, with higher current emissions resulting in lower future payoffs • Research questions: • Can socially optimal actions be sustained in this setting without an explicit enforcement mechanism? • Does access to history and advice from previous generations help to achieve and sustain optimality?

5. Some earlier exper. studies • Fischer et al (JEEM 2004) • Study altruistic restrain in common pool resource setting with dynamic externalitites • Chains of groups of subjects, each subject only made one decision • Report “optimistic free-riding” • Chaudhuri et al (ReStud 2006) • Study social learning and norms in a public good setting with intergenerational advice • Report that common knowledge of advice had a significant and positive effect on contributions

6. Model (close to Dutta and Radner) • - (non-overlapping) generation of players, starts with g=0 • i = 1..N - players (countries) • Each player’s payoff depends on the benefit from current activity xigand damage from the total emissions stock Sg: , where d is the damage from stock • Emissions stock for gen (g+1) depends on emissions by gen g • Stock retention rate: • First best solution:

7. Benchmark solutions Myopic Nash (MN): Each generation ignores dynamic externalities, maximizes own payoff Constant Markov Perfect (MP) A subgame perfect equilibrium of the dynamic game played by countries across generations First Best (FB) A cooperative solution with discounting δ<1 Sustainable (Sus) A cooperative solution with no discounting

8. Experimental Design • Dynamic externalities only, no static • Instead of choosing emission levels, subjects choose tokens, bounded [1,11] • 3-subject groups in each generation (“series”) • Total group tokens in this series determine the payoff level in the next series; this is emphasized in the instructions • Series 1 starts at the first best steady state stock • Extensive training before the actual play

9. Payoff Scenarios

10. Payoff Scenarios Continued

11. Experimental Design Continued At the end of each series, each subject sends a suggested number of tokens and verbal advice for the next series Advice and history from previous series is available Each series is continued to the next series with probability ¾ (determined by a roll of a die)

12. Experimental Treatments Baseline Long-Lived (LL) The same group of subjects makes decisions in all series represents an idealistic setting where long lived social planners make decision over a long time horizon and are motivated by long-term welfare for their countries Intergenerational Selfish (IS) In each series, decisions are made by a separate group of subjects, who are paid based on own series payoffs only. Groups are linked in chains. represents a more realistic setting in which the countries’ decision-makers are motivated more by their countries’ immediate welfare and may care at most partially about the future generations’ payoffs Intergenerational “Long-Sighted” (IL) In each series, decisions are made by a separate group of subjects, who are paid based on own series payoffs and all the followers’ payoffs

13. Results • We conducted Baseline Long-Lived (LL), Intergenerational Selfish (IS) and Intergenerational Long-Sighted (IL) treatments, with • 4-5 independent chains for each treatment, • 4-9 series (generations) per chain

14. Group Tokens by Series

15. Changes in Stock Level

16. Recommended Group Tokens

17. Summary of Results Baseline LL (Long-Lived) treatment: All groups were able to avoid myopic Nash solution and were converging to the First Best group tokens and stock levels Verbal advice was used as an effective communication device IS (Intergenerational Selfish) treatment: Group tokens and stock levels quickly increased to just under (but still below) the Myopic Nash levels Attempts made by some subjects to cut down group tokens were largely unsuccessful IL (Intergenerational Long-Sighted) treatment exhibited mixed dynamics in between the FB and MP benchmarks Based on the estimates of convergence levels, the difference between the treatments is significant in both actual decisions and stock, and advices

18. Advice from Baseline LL, Chain 2

19. Advice from IS Chain 4

20. Advice from IL Chain 3

21. Conclusions • We obtain evidence that self-interested individuals can resolve dynamic social dilemmas when interacting in small groups over a long time horizon (LL Treatment) • In an intergenerational setting without explicit motivation for caring for the future, (IS treatment), individual’s decisions are largely myopic • The evidence from the intergenerational IL treatment with full motivation for caring about the future is mixed; social dilemmas are not fully resolved This suggests that international dynamic enforcement mechanisms (treaties) are necessary to control GHG emissions

22. Decision Screen

23. Trial Results Screen

24. Series Results Screen

25. Waiting Screen with Advice

26. Advice from Previous Series