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.

Diss. (sammanfattning) Uppsala : Sveriges lantbruksuniv., 2008. / Härtill 4 uppsatser.

This thesis is composed of three chapters that form two parts. The first part is composed of two chapters and studies problems related to the exotic option market. In the first chapter we are interested in a numerical problem. More precisely we derive closed-form approximations for the price of some exotic options in the Black and Scholes framework. The second chapter discusses the construction of multivariate Lévy processes with and without stochastic volatility. The second part is composed of one chapter. It deals with a completely different issue. There we will study the problem of individual and temporal aggregation in panel data models. / Doctorat en sciences économiques, Orientation économie / info:eu-repo/semantics/nonPublished

Doctorat en sciences sociales, politiques et économiques / info:eu-repo/semantics/nonPublished

LLM / Department of Mercantile Law / The study critically analyses the concurrent enforceability of restraint of trade and garden leave in South African Labour law. The study seeks to answer the question of whether or not the simultaneous enforceability of restraint of trade agreements and garden leave is reasonable. Designed within a qualitative paradigm primarily based on a critical literature review, the study employs a doctrinal approach to establish the contemporary legal position in respect of the simultaneous enforceability of restraint of trade agreements and garden leave in South African Labour law. The objectives pursued by the study are to mitigate the controversies and clear the confusion relating to the enforceability of restraint of trade agreements; to justify the doctrine of restraint of trade; assess the reasonableness of the simultaneous enforceability of garden leave and restraint of trade; examine the onus of proof in matters dealing with the enforceability of restraint of trade agreements; test the constitutionality of restraint of trade agreements; evaluate the relationship between restraint of trade agreements and garden leave; and propose practical recommendations that can be employed to address identified legal flaws in the context of the topic. Structurally, the study begins with unpacking the background to the research topic, the history, origin and rationality of restraint of trade agreements together with an assessment of their enforceability. It further examines the effect of garden leave on restraint of trade agreements, outlines comparative perspectives on restraint of trade, including aspects relating to garden leave and highlights lessons South Africa may learn from the selected jurisdictions. Finally, the study recommends that South African jurisprudence should be developed to shift the burden of proof to employers to prove reasonableness of garden leave and restraint of trade agreements, to impose an obligation on employers to pay former employees for rendering them jobless and to set a maximum period that an employee can be prevented to compete or be employed by employer’s competitors. / NRF

近來共享經濟商業模式崛起，對各國既有相關市場皆造成不少之衝擊，當中，優步公司用以計算車資之「訂價演算法」，於美國實務亦引起許多爭議。美國司法案例中其中一個重要爭議即為優步公司單方制定之「訂價演算法」與其採行之「高峰動態訂價法」究否構成價格聯合行為。於美國實務近來2起與價格聯合行為相關之案例，即包含Meyer v. Kalanick案與Chamber of Commerce & RASIER, LLC v. City of Seattle案（以下簡稱「City of Seattle案」）中，皆可見Uber企圖正當化其價格聯合行為，以免於競爭法審查下有違法之嫌。而美國對於價格聯合行為之規範，載明於休曼法第1條；依據休曼法第1條規定，若原告擬主張被告行為違反卡特爾行為，則應證明系爭卡特爾行為符合合意主體要件、具合意或共謀行為，與造成限制性之競爭效果等三項要件。由於上述二案皆仍於訴訟前階段，判決尚未出爐，因此，此議題值得吾等分析之。本文擬以美國實務判決為基準，彙整相關爭議，進而探討Uber所採訂價演算法是否構成價格聯合行為。 本文發現，雖然此等訂價演算法究否構成價格聯合行為尚未有定論，然由於訂價演算法中之高峰動態訂價法可提高駕駛於尖峰時段中提供載客服務之誘因，將有助於調節市場機制與促進競爭。此外，Uber亦可利用其訂價演算法與設置平台所奠立之優勢，使其得以潛在破壞市場秩序之形式，創造競爭優勢。據此，Uber除可克服既有行政管制下市場進入之劣勢外，亦得使相關市場交易效率大幅提升、市場更加競爭。因此，於探討Uber價格聯合行為合法與否時，亦應將此等因素納入考量。 / The rapid expansion of sharing economy enterprises around the world has led to many challenges. And among these enterprises, one of the most disruptive examples is Uber because of its algorithm. In the United States, the lawsuits regarding Uber's algorithm has also gained massive attention. One of the controversial issues of the complaints relies upon whether Uber's algorithm which set by Uber, and “surge pricing” model do constitute an illegal price-fixing in violation of Section 1 of the Sherman Act. In 2 recent high-profile cases, Meyer v. Kalanick & Chamber of Commerce & RASIER, LLC v. City of Seattle, Uber has tried to justify its price fixing to avoid antitrust scrutiny. There are three specific facts that the Plaintiff must prove to establish its antitrust claim in Section 1 of the Sherman Act: 2 or more entities entering into an agreement, conspiracy, and unreasonably restrains competition. Analysis regarding Uber's algorithm is significant because the trials are ongoing. Therefore, the thesis examines whether Uber's algorithm do constitute an illegal price-fixing in violation of Section 1 of the Sherman Act by exploring the potential problems with regard to the elements based on U.S. judicial decisions. The thesis believes that Uber's algorithm can enhance the efficiency of transaction and has pro-competitive effects, leading to the impact of Uber's surge pricing on providing the incentives for drivers during peak hours. Establishing platform and Uber's algorithm create Uber's strengths and advantages. By having disrupted the existing industry, Uber's algorithm serves pro-competitive purposes.