Game Theory Applied To The Brazilian Antitrust Leniency Program

Leniency and cooperation programs are considered important instruments to assist in the effectiveness of the antitrust policy and they are designed to attend the following purposes: in the short run to facilitate detection of cartels and in the long run to destabilize existing cartels and deter the appearance of new ones. This study aims to analyze empirically whether the Brazilian leniency program has been achieving these goals. To this end, it tests the hypotheses developed by Brenner (2009) to assess the effectiveness of the EU leniency program.

In recent article on the nineteenth-century North American fur trade Ann Carlos and Elizabeth Hoffman interpret negotiations between the Hudson's Bay Company (HBC) and the Northwest Company (NWC) through the application of formal bargaining models.' The article is valuable contribution to our understanding of the negotiations that eventually resulted in the merger between the Hudson's Bay Company and the Northwest Company, but its authors have the more ambitious goal of providing a framework for studying other duopolies (or small groups of firms) in economic history.' 2 Their aim is to clarify economic history by analyzing this historical event within framework of two-person bargaining under imperfect information. I suggest, however, that Carlos and Hoffman have not been altogether successful. Although they study an interesting historical case, they invoke an inadequate and inappropriate framework that distracts from the relevant historical issues. It is easy to see why the story of the North American fur trade negotiations would interest economic historians. Between 1804 and 1821 the Hudson's Bay Company and the Northwest Company were duopoly protected from outside competition and unhindered by antitrust legislation. They operated in the same geographical region and in the same markets. Carlos and Hoffman ask why the two companies failed to merge for the better part of quarter-century despite realizing that merger would be mutually beneficial. Although the merger eventually took place, it came only after protracted negotiation which dissipated most of the rents. Previous studies have modeled the fur trade in non-cooperative (Prisoners' Dilemma) terms, but, Carlos and Hoffman argue, there were too many attempts at cooperation to make that framework appropriate here.3 The two firms made numerous gestures of cooperation, were legally capable of signing an agreement, and were in constant negotiations for merger. But if cooperative game theory is the appropriate analytic structure, we are confronted with paradox. The two firms resisted merger despite the obvious advantages and in contradiction to the predictions of simple model of cooperative game. Carlos and Hoffman suggest resolution by appealing to more complicated models of bargaining. Making use of the theoretical work of Vincent Crawford and Roger Myerson, the authors conclude that the achievement of joint maximum was hindered by incomplete information, commitment to strategy which led

We consider dynamic cooperative games, where the worth of coalitions varies over time according to the history of allocations. When defining the core of a dynamic game, we allow the possibility for coalitions to deviate at any time and thereby to give rise to a new environment. A coalition that considers a deviation needs to take the consequences into account because from the deviation point on, the game is no longer played with the original set of players. The deviating coalition becomes the new grand coalition which, in turn, induces a new dynamic game. The stage games of the new dynamical game depend on all previous allocation including those that have materialized from the deviating time on. We define three types of core solutions: fair core, stable core and credible core. We characterize the first two in case where the instantaneous game depends on the last allocation (rather than on the whole history of allocations) and the third in the general case. The analysis and the results resembles to a great extent the theory of non-cooperative dynamic games.

The nodes in Ad Hoc networks compete for channels when communicating, with the features of no center and self-organization. In traditional channel assignment strategy of MAC layer, nodes do not consider the demands to channel resources of other nodes, which hinders improving the network performance. Practically, in the network based on competitive MAC protocol, each node tries to maximize its payoff, while this interferes with the behavior of other nodes at the same time. Game theory is an effective tool to solve problems of distributed resources, which can be used effectively in channel assignment. In this paper, we propose a new protocol, namely, DGPCI-DCA (Dynamic Game with Perfect and Complete Information based Dynamic Channel Assignment). When all the nodes are rational and greedy, each node selects channels dynamically by backward induction according to strategies of other nodes, thus Nash equilibrium can finally be achieved. Experiments show that the network performance is effectively improved, i.e., the throughput and saturation throughput can be increased, and the packet loss rate and network delay can be reduced.

Recently, crypto-cloud computing has become an interesting research area with many technical, security, commercial and financial aspects, goals and consequences. The original intention of the cloud is to link computing stations (services) for collaboration. Sharing and coordination of computational resources is important because of the activities between service providers and service requesters. Considering the cooperative functionality of crypto-cloud computing, the use of game theory in that area has became very beneficial. In the sequel, we mathematically associate that area with game theory, i.e., the bargaining and compromising of interests of various “players”, by using a game-theoretical approach which arises from networks, servers, operating systems, storage devices, etc. Further, we propose a novel efficient encryption system by using XTR (effective and compact subgroup trace representation) which has the property of semantic security. Those interactions has been constructed in the direction of how cryptographic tools can be used to address a natural optimization problem in the fields of game theory and financial economics. It is believed that game theory and its optimization is going to provide a suitable framework for the design of a crypto-cloud computing system that will be perceived as a strong technique and satisfy the needs of many participants and users of the cloud. The paper ends with a conclusion and an outlook to future studies.

Study of game models and the complex dynamics of a low-carbon supply chain with an altruistic retailer under consumers’ low-carbon preference

Because of the nature of high mobility and dynamic network topology, Intrusion Detection Systems (IDSs) in Vehicular Ad-hoc Networks (VANETs) face the challenge in balancing the accuracy and efficiency of detection. Two crucial problems are remained unsolved in existing studies: 1) how to perceive the environmental change in the perspective of an IDS? 2) how to make the IDS adaptive in different scenarios? In this paper, a self-adaptive scheme is proposed for IDSs in VANETs based on Bayesian game theory and Deep Q-learning Network (DQN). In the scheme, the interactions between an IDS and attackers are formulated as a dynamic intrusion detection game, in which the proposed scheme decides either to just adjust or to completely retrain the IDS. The Nash Equilibria (NE) of the game is derived to reveal how the optimal decision of the IDS depends on the detection performance and road conditions. Moreover, a DQN-Adjustment is proposed to realize the self-adaptation of the IDS in the dynamic game. Simulation results show that the IDS with the proposed self-adaptive scheme has better performance than other existing IDSs with higher detection rate as well as lower detection time and overhead.

This paper proposes a two-person multi-stage zero-sum game model considering the confrontation between cascading failures and control strategies in an AC/DC hybrid system to solve the blocking problem of DC systems caused by successive failures at the receiving end of an AC/DC system. A game model is established between an attacker (power grid failure) and a defender (dispatch side). From the attacker’s perspective, this study mainly investigates the problem of system line failures caused by AC or DC blockages. From the perspective of dispatch-side defense, the multiple-feed short-circuit ratio constraint method, output adjustment measures of the energy storage system, sensitivity control, and distance third-segment protection adjustment are used as strategies to reduce system losses. Using as many line return data as possible as samples, the deep Q-network (DQN), a deep reinforcement learning algorithm, is used to obtain the Nash equilibrium of the game model. The corresponding optimal dispatch and defense strategies are also obtained while obtaining the optimal sequence of tripping failures for AC/DC hybrid system cascading failures. Using the improved IEEE 39-node system as an example, the simulation results verify the appropriateness of the two-stage dynamic zero-sum game model to schedule online defense strategies and the effectiveness and superiority of the energy storage system participating in defense adjustment.

The traveler's dilemma is a well studied situation in the field of game theory in which the 2 players achieve very poor payoffs by each adhering to the Nash equilibrium strategy. Paradoxically, by deviating from the “rational” strategy, a significantly better payoff can be achieved, and it has been shown that humans overwhelmingly choose these “irrational” strategies. In this paper we (1) show that a single large assumption underlies the rational analysis which makes the Nash equilibrium rationally deducible and that (2) relaxing this assumption causes the deduction of the Nash equilibrium to fail. Therefore, we (3) introduce early work proposing an extension to game theory called fuzzy weak domination. Using fuzzy weak domination, we perform a rational analysis in which the opponent rationality assumption is regarded as uncertain and (4) show that the emergent equilibrium may more closely capture human behaviour.

Finite-dimensional, time invariant, linear quadratic dynamic games are perhaps the best understood and researched class of dynamic games. This is particularly true for continuous-time linear quadratic differential games. In this paper, the application of the theory of dynamic games to signal processing is considered. We are interested in digital signal processing and therefore we confine our attention to discrete-time linear-quadratic dynamic games (LQDG). In discrete-time the cost function contains product terms between the decision variables which complicates the analysis compared to its continuous-time analogue. With a view to facilitate the application of the theory of dynamic games to digital signal processing, and in particular, disturbance rejection, the complete solution of the discrete-time LQDG is worked out and explicit results are obtained. Thus,discrete-time LQDGs have the distinct advantage of being amenable to analysis, closed-form solutions are possible, and one is in tune with modern digital signal processing techniques. In this paper, minimal necessary and sufficient conditions for the existence of a solution to the discrete-time LQDG are provided and its explicit, closed-form, solution is worked out. This opens the way to designing novel digital signal processing algorithms for disturbance rejection. Information plays a critical role in game theory and in particular in dynamic games. Using our explicit solution of the deterministic LQDG, a hierarchy of three zero-sum stochastic LQDGs characterized by a sequence of information patterns which increase in complexity is analyzed.

The attack and defense model of network security based on game theory has developed into an effective network security model. It is a hot topic in the field of network and information security to use the idea of game theory to attack and defend the network security. This paper analyzes the general model of game theory in the field of security. This paper also analyzes the general model of network security attack and defense game. This paper also analyzes the game theory model of cryptographic protocol. This paper studies the existing network security methods based on game theory, reviews the development and evolution process and technical core of network security based on game theory, and compares the specificity of network security models based on game theory. Finally, the paper discusses the problems in the field of network security based on game theory, and tries to forecast its future development trend.

Cooperative strategies have the great potential of improving network performance and spectrum utilization in future networking environments. This new paradigm in terms of network management, however, requires a novel design and analysis framework targeting a highly flexible networking solution with a distributed architecture. Game Theory is very suitable for this task, since it is a comprehensive mathematical tool for modeling the highly complex interactions among distributed and intelligent decision makers. In this way, the more convenient management policies for the diverse players (e.g. content providers, cloud providers, home providers, brokers, network providers or users) should be found to optimize the performance of the overall network infrastructure. The authors discuss in this chapter several Game Theory models/concepts that are highly relevant for enabling collaboration among the diverse players, using different ways to incentivize it, namely through pricing or reputation. In addition, the authors highlight several related open problems, such as the lack of proper models for dynamic and incomplete information games in this area.

Nonlinear dynamics of discrete time multi-level leader–follower games

Provides a game theoretical perspective on the problem of international pollution control. Presents background for those unfamiliar with game theory. Explores how cooperation can be established between countries to fight international pollution, analyzing static games with discrete strategy space; finite dynamic games with discrete strategy space; infinite dynamic games with discrete strategy space; whether, and under what conditions, the linkage of two or more issues may be conducive to cooperation; a simple static global emission game with continuous strategy space; finite dynamic games with continuous strategy space and static representations of dynamic games; bargaining over a uniform emission reduction quota and a uniform emission tax; the global emission game in the context of an infinite time horizon; and the coalition formation process in an N-country world.

Discrete time spatial models arising in genetics, evolutionary game theory, and branching processes.