Models of Cooperation, Learning and Catastrophic Risk
Doktorsavhandling, 2016

Our world presents us with dangers and opportunities. Some of these dangers and opportunities are easier to handle if two or more individuals learn to cooperate. This thesis contributes five papers about cooperation, learning and catastrophic risk. In papers I-II, we consider the Finitely Repeated Prisoners' Dilemma, a model for where cooperation between two players is particularly hard to achieve. We introduce and model strategies that attempt to convince others to cooperate when backward induction can be used to eliminate cooperation for a number of steps from the end. We find that in a population with these strategies, cooperation can become recurrent, and we examine the conditions for this. Recurrent cooperation is possible in an evolutionary model (paper I) as well as in a population of players that are near-perfect Bayesian expected utility-maximizers (paper II). In paper III, we consider a bargaining model of climate negotiations where players negotiate emissions and sudden catastrophic damage occurs if emissions exceed a threshold amount. We introduce and model a mechanism of strategic reasoning, where players predict the emission bids of others, and consider how this affects the possibility of reaching agreements preventing catastrophic damage. We find that the effect of higher levels of strategic reasoning makes it harder to reach agreements in the model. This effect can be partially mitigated by restricting the range of initial bids in the bargaining process. In paper IV, we consider the arguments by Hanson and Bostrom about the Great Filter as an attempt to explain the Fermi Paradox. According to these arguments, finding extraterrestrial life on one planet should lower our expectations for humanity's prospects to progress far beyond our current technological capabilities. We model this claim as a Bayesian learning problem and examine the effect a single observation of life has in the model. We find that the conclusion of the argument depends critically on the choice of prior distribution. In paper V, we consider a model of agricultural markets and land-use competition between food and bioenergy crops. Agents in the model represent farmers who decide which crop to grow depending on predictors that give future price expectations. We model agents who can switch among predictors to make their decisions. We find that some predictor types can be concentrated on key parcels of land, which reduces volatility in crop prices for the system. We also examine several mechanisms that can bring price fluctuations in the system down and closer to a stable state.

Backward Induction

Bayesian analysis

Finitely Repeated Prisoners' Dilemma

Cooperation

Fermi Paradox

Learning

Climate negotiations

Catastrophic risk

ED
Opponent: Kimmo Eriksson, Professor, Tilllämpad matematik, Mälardalens högskola, Västerås

Författare

Vilhelm Verendel

Chalmers, Energi och miljö, Fysisk resursteori

Our world presents us with dangers and opportunities. Some of these dangers and opportunities are easier to handle when two or more individuals cooperate. One example is climate change: Current climate change is already dangerous, and the danger is increasing [62], but climate negotiations also present opportunities to establish cooperation and mitigate damage (paper III). Collective action problems where cooperation is particularly hard to achieve can be explored using models such as the Finitely Repeated Prisoners' Dilemma, which can be used to reason about how cooperation can be established between two individuals (papers I and II). Further, models of learning can also shed light on how to interpret the Great Filter arguments -- a response to the Fermi Paradox -- about why, so far, we have yet seen no signs of extraterrestrial life. According to these arguments, finding extraterrestrial life on one planet should lower our expectations for humanity's prospects to progress far beyond our current technological capabilities [19,42]. Bayesian learning makes it possible to analyze whether the conclusion of these arguments hold under different assumptions (paper IV). Learning can also affect the volatility of prices in environments such as commodity markets (paper V).

Our world presents us with dangers and opportunities. Some of these dangers and opportunities are easier to handle when two or more individuals cooperate. One example is climate change: Current climate change is already dangerous, and the danger is increasing [62], but climate negotiations also present opportunities to establish cooperation and mitigate damage (paper III). Collective action problems where cooperation is particularly hard to achieve can be explored using models such as the Finitely Repeated Prisoners' Dilemma, which can be used to reason about how cooperation can be established between two individuals (papers I and II). Further, models of learning can also shed light on how to interpret the Great Filter arguments -- a response to the Fermi Paradox -- about why, so far, we have yet seen no signs of extraterrestrial life. According to these arguments, finding extraterrestrial life on one planet should lower our expectations for humanity's prospects to progress far beyond our current technological capabilities [19,42]. Bayesian learning makes it possible to analyze whether the conclusion of these arguments hold under different assumptions (paper IV). Learning can also affect the volatility of prices in environments such as commodity markets (paper V).

Ämneskategorier

Evolutionsbiologi

Psykologi (exklusive tillämpad psykologi)

Astronomi, astrofysik och kosmologi

Statsvetenskap (exklusive studier av offentlig förvaltning och globaliseringsstudier)

Energisystem

Sannolikhetsteori och statistik

Klimatforskning

Styrkeområden

Informations- och kommunikationsteknik

Transport

Energi

Drivkrafter

Hållbar utveckling

Fundament

Grundläggande vetenskaper

ISBN

978-91-7597-349-4

Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie

ED

Opponent: Kimmo Eriksson, Professor, Tilllämpad matematik, Mälardalens högskola, Västerås