Wholesale electricity markets now operate in many countries around the world. These markets determine a spot price for electricity as the clearing price when generators bid in energy at various prices. As the trading in a wholesale electricity market can be seen as a dynamic repeated game, it would be expected that profit maximising generators learn to engage in tacit collusion to profitably increase spot market prices. This thesis investigates this tacit collusion of generators in oligopolistic electricity markets. We do not follow the approach of previous work in game theory that presupposes firms' collusive strategies to enforce collusion in an oligopoly. Instead, we develop a co-evolutionary approach (extending previous work in this area) using a genetic algorithm (GA) to co-evolve strategies for all generators in some stylised models of an electricity market. The bidding strategy of each generator is modelled as a set of bidding actions, one for each possible discrete state of the state space observed by the generator. The market trading interactions are simulated to determine the fitness of a particular strategy. The tacitly collusive outcomes and strategies emerging from computational experiments are thus obtained from the learning or evolutionary process instead of from any pre-specification. Analysing many of those emergent collusive outcomes and strategies. we are able to specify the mechanism of tacit collusion and investigate how the market environment can affect it. We find that the learned collusive strategies are similar to the forgiving trigger strategies of classical supergame theory (Green and Porter, 1984). Also using computational experiments, we can determine which characteristics of the market environment encourage or hinder tacit collusion. The findings from this thesis provide insights on tacit collusion in an oligopoly and policy implications from a learning perspective. With modelling flexibility, our co-evolutionary approach can be extended to study strategic behaviour in an oligopoly considering many other market characteristics.

Wholesale electricity markets now operate in many countries around the world. These markets determine a spot price for electricity as the clearing price when generators bid in energy at various prices. As the trading in a wholesale electricity market can be seen as a dynamic repeated game, it would be expected that profit maximising generators learn to engage in tacit collusion to profitably increase spot market prices. This thesis investigates this tacit collusion of generators in oligopolistic electricity markets. We do not follow the approach of previous work in game theory that presupposes firms' collusive strategies to enforce collusion in an oligopoly. Instead, we develop a co-evolutionary approach (extending previous work in this area) using a genetic algorithm (GA) to co-evolve strategies for all generators in some stylised models of an electricity market. The bidding strategy of each generator is modelled as a set of bidding actions, one for each possible discrete state of the state space observed by the generator. The market trading interactions are simulated to determine the fitness of a particular strategy. The tacitly collusive outcomes and strategies emerging from computational experiments are thus obtained from the learning or evolutionary process instead of from any pre-specification. Analysing many of those emergent collusive outcomes and strategies. we are able to specify the mechanism of tacit collusion and investigate how the market environment can affect it. We find that the learned collusive strategies are similar to the forgiving trigger strategies of classical supergame theory (Green and Porter, 1984). Also using computational experiments, we can determine which characteristics of the market environment encourage or hinder tacit collusion. The findings from this thesis provide insights on tacit collusion in an oligopoly and policy implications from a learning perspective. With modelling flexibility, our co-evolutionary approach can be extended to study strategic behaviour in an oligopoly considering many other market characteristics.

Wholesale electricity markets now operate in many countries around the world. These markets determine a spot price for electricity as the clearing price when generators bid in energy at various prices. As the trading in a wholesale electricity market can be seen as a dynamic repeated game, it would be expected that profit maximising generators learn to engage in tacit collusion to profitably increase spot market prices. This thesis investigates this tacit collusion of generators in oligopolistic electricity markets. We do not follow the approach of previous work in game theory that presupposes firms' collusive strategies to enforce collusion in an oligopoly. Instead, we develop a co-evolutionary approach (extending previous work in this area) using a genetic algorithm (GA) to co-evolve strategies for all generators in some stylised models of an electricity market. The bidding strategy of each generator is modelled as a set of bidding actions, one for each possible discrete state of the state space observed by the generator. The market trading interactions are simulated to determine the fitness of a particular strategy. The tacitly collusive outcomes and strategies emerging from computational experiments are thus obtained from the learning or evolutionary process instead of from any pre-specification. Analysing many of those emergent collusive outcomes and strategies. we are able to specify the mechanism of tacit collusion and investigate how the market environment can affect it. We find that the learned collusive strategies are similar to the forgiving trigger strategies of classical supergame theory (Green and Porter, 1984). Also using computational experiments, we can determine which characteristics of the market environment encourage or hinder tacit collusion. The findings from this thesis provide insights on tacit collusion in an oligopoly and policy implications from a learning perspective. With modelling flexibility, our co-evolutionary approach can be extended to study strategic behaviour in an oligopoly considering many other market characteristics.

Wholesale electricity markets now operate in many countries around the world. These markets determine a spot price for electricity as the clearing price when generators bid in energy at various prices. As the trading in a wholesale electricity market can be seen as a dynamic repeated game, it would be expected that profit maximising generators learn to engage in tacit collusion to profitably increase spot market prices. This thesis investigates this tacit collusion of generators in oligopolistic electricity markets. We do not follow the approach of previous work in game theory that presupposes firms' collusive strategies to enforce collusion in an oligopoly. Instead, we develop a co-evolutionary approach (extending previous work in this area) using a genetic algorithm (GA) to co-evolve strategies for all generators in some stylised models of an electricity market. The bidding strategy of each generator is modelled as a set of bidding actions, one for each possible discrete state of the state space observed by the generator. The market trading interactions are simulated to determine the fitness of a particular strategy. The tacitly collusive outcomes and strategies emerging from computational experiments are thus obtained from the learning or evolutionary process instead of from any pre-specification. Analysing many of those emergent collusive outcomes and strategies. we are able to specify the mechanism of tacit collusion and investigate how the market environment can affect it. We find that the learned collusive strategies are similar to the forgiving trigger strategies of classical supergame theory (Green and Porter, 1984). Also using computational experiments, we can determine which characteristics of the market environment encourage or hinder tacit collusion. The findings from this thesis provide insights on tacit collusion in an oligopoly and policy implications from a learning perspective. With modelling flexibility, our co-evolutionary approach can be extended to study strategic behaviour in an oligopoly considering many other market characteristics.

Wholesale electricity markets now operate in many countries around the world. These markets determine a spot price for electricity as the clearing price when generators bid in energy at various prices. As the trading in a wholesale electricity market can be seen as a dynamic repeated game, it would be expected that profit maximising generators learn to engage in tacit collusion to profitably increase spot market prices. This thesis investigates this tacit collusion of generators in oligopolistic electricity markets. We do not follow the approach of previous work in game theory that presupposes firms' collusive strategies to enforce collusion in an oligopoly. Instead, we develop a co-evolutionary approach (extending previous work in this area) using a genetic algorithm (GA) to co-evolve strategies for all generators in some stylised models of an electricity market. The bidding strategy of each generator is modelled as a set of bidding actions, one for each possible discrete state of the state space observed by the generator. The market trading interactions are simulated to determine the fitness of a particular strategy. The tacitly collusive outcomes and strategies emerging from computational experiments are thus obtained from the learning or evolutionary process instead of from any pre-specification. Analysing many of those emergent collusive outcomes and strategies. we are able to specify the mechanism of tacit collusion and investigate how the market environment can affect it. We find that the learned collusive strategies are similar to the forgiving trigger strategies of classical supergame theory (Green and Porter, 1984). Also using computational experiments, we can determine which characteristics of the market environment encourage or hinder tacit collusion. The findings from this thesis provide insights on tacit collusion in an oligopoly and policy implications from a learning perspective. With modelling flexibility, our co-evolutionary approach can be extended to study strategic behaviour in an oligopoly considering many other market characteristics.

Wholesale electricity markets now operate in many countries around the world. These markets determine a spot price for electricity as the clearing price when generators bid in energy at various prices. As the trading in a wholesale electricity market can be seen as a dynamic repeated game, it would be expected that profit maximising generators learn to engage in tacit collusion to profitably increase spot market prices. This thesis investigates this tacit collusion of generators in oligopolistic electricity markets. We do not follow the approach of previous work in game theory that presupposes firms' collusive strategies to enforce collusion in an oligopoly. Instead, we develop a co-evolutionary approach (extending previous work in this area) using a genetic algorithm (GA) to co-evolve strategies for all generators in some stylised models of an electricity market. The bidding strategy of each generator is modelled as a set of bidding actions, one for each possible discrete state of the state space observed by the generator. The market trading interactions are simulated to determine the fitness of a particular strategy. The tacitly collusive outcomes and strategies emerging from computational experiments are thus obtained from the learning or evolutionary process instead of from any pre-specification. Analysing many of those emergent collusive outcomes and strategies. we are able to specify the mechanism of tacit collusion and investigate how the market environment can affect it. We find that the learned collusive strategies are similar to the forgiving trigger strategies of classical supergame theory (Green and Porter, 1984). Also using computational experiments, we can determine which characteristics of the market environment encourage or hinder tacit collusion. The findings from this thesis provide insights on tacit collusion in an oligopoly and policy implications from a learning perspective. With modelling flexibility, our co-evolutionary approach can be extended to study strategic behaviour in an oligopoly considering many other market characteristics.

Wholesale electricity markets now operate in many countries around the world. These markets determine a spot price for electricity as the clearing price when generators bid in energy at various prices. As the trading in a wholesale electricity market can be seen as a dynamic repeated game, it would be expected that profit maximising generators learn to engage in tacit collusion to profitably increase spot market prices. This thesis investigates this tacit collusion of generators in oligopolistic electricity markets. We do not follow the approach of previous work in game theory that presupposes firms' collusive strategies to enforce collusion in an oligopoly. Instead, we develop a co-evolutionary approach (extending previous work in this area) using a genetic algorithm (GA) to co-evolve strategies for all generators in some stylised models of an electricity market. The bidding strategy of each generator is modelled as a set of bidding actions, one for each possible discrete state of the state space observed by the generator. The market trading interactions are simulated to determine the fitness of a particular strategy. The tacitly collusive outcomes and strategies emerging from computational experiments are thus obtained from the learning or evolutionary process instead of from any pre-specification. Analysing many of those emergent collusive outcomes and strategies. we are able to specify the mechanism of tacit collusion and investigate how the market environment can affect it. We find that the learned collusive strategies are similar to the forgiving trigger strategies of classical supergame theory (Green and Porter, 1984). Also using computational experiments, we can determine which characteristics of the market environment encourage or hinder tacit collusion. The findings from this thesis provide insights on tacit collusion in an oligopoly and policy implications from a learning perspective. With modelling flexibility, our co-evolutionary approach can be extended to study strategic behaviour in an oligopoly considering many other market characteristics.