Modeling Electricity Markets as Two-Stage Capacity Constrained Price Competition Games under Uncertainty

The last decade has seen an increasing application of game theoretic tools in the analysis of electricity markets and the strategic behavior of market players. This paper focuses on the model examined by Fabra et al. (2008), where the market is described by a two-stage game with the firms choosing their capacity in the first stage and then competing in prices in the second stage. By allowing the firms to endogenously determine their capacity, through the capacity investment stage of the game, they can greatly affect competition in the subsequent pricing stage. Extending this model to the demand uncertainty case gives a very good candidate for modeling the strategic aspect of the investment decisions in an electricity market. After investigating the required assumptions for applying the model in electricity markets, we present some numerical examples of the model on the resulting equilibrium capacities, prices and profits of the firms. We then proceed with two results on the minimum value of price caps and the minimum required revenue from capacity mechanisms in order to induce adequate investments

A data-driven game theoretic strategy for developers in software crowdsourcing: a case study

Crowdsourcing has the advantages of being cost-effective and saving time, which is a typical embodiment of collective wisdom and community workers’ collaborative development. However, this development paradigm of software crowdsourcing has not been used widely. A very important reason is that requesters have limited knowledge about crowd workers’ professional skills and qualities. Another reason is that the crowd workers in the competition cannot get the appropriate reward, which affects their motivation. To solve this problem, this paper proposes a method of maximizing reward based on the crowdsourcing ability of workers, they can choose tasks according to their own abilities to obtain appropriate bonuses. Our method includes two steps: Firstly, it puts forward a method to evaluate the crowd workers’ ability, then it analyzes the intensity of competition for tasks at Topcoder.com—an open community crowdsourcing platform—on the basis of the workers’ crowdsourcing ability; secondly, it follows dynamic programming ideas and builds game models under complete information in different cases, offering a strategy of reward maximization for workers by solving a mixed-strategy Nash equilibrium. This paper employs crowdsourcing data from Topcoder.com to carry out experiments. The experimental results show that the distribution of workers’ crowdsourcing ability is uneven, and to some extent it can show the activity degree of crowdsourcing tasks. Meanwhile, according to the strategy of reward maximization, a crowd worker can get the theoretically maximum reward

Games judges don't play: predatory pricing and strategic reasoning in US antitrust

The paper analyzes the last three decades of debates on predatory pricing in US antitrust law, starting from the literature which followed Areeda & Turner 1975 and ending with the early years of the new century, after the Brooke decision. Special emphasis is given to the game-theoretic approach to predation and to the reasons why this approach has never gained attention in courtrooms. It is argued that, despite their mathematical rigor, the sophisticated stories told by strategic models in order to demonstrate the actual viability of predatory behavior fail to satisfy the criteria which guide the decisions of antitrust courts, in particular their preference for easy-to-apply rules. Therefore predation cases are still governed by a peculiar alliance between Chicago-style price theory – which, contrary to game theory, considers predatory behavior almost always irrational – and a Harvard-style attention for the operational side of antitrust enforcement.Antitrust law; predatory pricing; Chicago School; Harvard; game theory

Ideal Free Distributions, Evolutionary Games, and Population Dynamics in Multiple-Species Environments

In this article, we develop population game theory, a theory that combines the dynamics of animal behavior with population dynamics. In particular, we study interaction and distribution of two species in a two-patch environment assuming that individuals behave adaptively (i.e., they maximize Darwinian fitness). Either the two species are competing for resources or they are in a predator-prey relationship. Using some recent advances in evolutionary game theory, we extend the classical ideal free distribution (IFD) concept for single species to two interacting species. We study population dynamical consequences of two-species IFD by comparing two systems: one where individuals cannot migrate between habitats and one where migration is possible. For single species, predator-prey interactions, and competing species, we show that these two types of behavior lead to the same population equilibria and corresponding species spatial distributions, provided interspecific competition is patch independent. However, if differences between patches are such that competition is patch dependent, then our predictions strongly depend on whether animals can migrate or not. In particular, we show that when species are settled at their equilibrium population densities in both habitats in the environment where migration between habitats is blocked, then the corresponding species spatial distribution need not be an IFD. Thus, when species are given the opportunity to migrate, they will redistribute to reach an IFD (e.g., under which the two species can completely segregate), and this redistribution will also influence species population equilibrial densities. Alternatively, we also show that when two species are distributed according to the IFD, the corresponding population equilibrium can be unstable

The ratchet effect in a two lag setting and the mitigating influence of yardstick competition

In order to increase efficiency in the provision of power distribution networks, the German regulator Bundesnetzagentur plans to implement revenue cap regulation together with yardstick competition. Revenue cap regulation could bear the ratchet effect: cost minimization need not to be optimal for the operator who anticipates that his revenue cap will become adjusted according to his cost performance. The regulator could extract all the rent by lowering an operator’s revenue cap to the level of costs he revealed to be possible for him to reach. The ratchet effect could be mitigated by yardstick competition at which the level of revenues that is allowed to one operator is tied to the performance of others that are comparable to him. One will only be allowed to accumulate revenues that recover the least cost level that has been adopted within the group of comparable decision makers. In a setting of two sequential regulatory lags, this paper examines the occurence of the ratchet effect and the mitigating influence that yardstick competition has on it.ratchet effect, yardstick competition, regulation

Load Shifting in the Smart Grid: To Participate or Not?

Demand-side management (DSM) has emerged as an important smart grid feature that allows utility companies to maintain desirable grid loads. However, the success of DSM is contingent on active customer participation. Indeed, most existing DSM studies are based on game-theoretic models that assume customers will act rationally and will voluntarily participate in DSM. In contrast, in this paper, the impact of customers' subjective behavior on each other's DSM decisions is explicitly accounted for. In particular, a noncooperative game is formulated between grid customers in which each customer can decide on whether to participate in DSM or not. In this game, customers seek to minimize a cost function that reflects their total payment for electricity. Unlike classical game-theoretic DSM studies which assume that customers are rational in their decision-making, a novel approach is proposed, based on the framework of prospect theory (PT), to explicitly incorporate the impact of customer behavior on DSM decisions. To solve the proposed game under both conventional game theory and PT, a new algorithm based on fictitious player is proposed using which the game will reach an epsilon-mixed Nash equilibrium. Simulation results assess the impact of customer behavior on demand-side management. In particular, the overall participation level and grid load can depend significantly on the rationality level of the players and their risk aversion tendency.Comment: 9 pages, 7 figures, journal, accepte