Online Advertising

This chapter explores what makes online advertising different from traditional advertising channels. We argue that online advertising differs from traditional advertising channels in two important ways: measurability and targetability. Measurability is higher because the digital nature of online advertising means that responses to ads can be tracked relatively easily. Targetability is higher because data can be automatically tracked at an individual level, and it is relatively easy to show different people different ads. We discuss recent advances in search advertising, display advertising, and social media advertising and explore the key issues that arise for firms and consumers from measurability and targetability. We then explore possible public policy consequences, with an in depth discussion of the implications for consumer privacy

Evolutionary non-cooperative spectrum sharing game: long-term coexistence for collocated cognitive radio networks

Collocated cognitive radio networks (CRNs) employ coexistence protocols to share the spectrum when it is not being used by the licensed primary users. These protocols work under the assumption that all spectrum bands provide the same level of quality of service, which is somewhat simplistic because channel conditions as well as the licensee\u27s usage of allocated channels can vary significantly with time and space. These circumstances dictate that some channels may be considered better than others; therefore, CRNs are expected to have a preference over the choice of available channels. Because all CRNs are assumed to be rational and select the best available channels, it can lead to an imbalance in contention for disparate channels, degraded quality of service, and an overall inefficient utilization of spectrum resource. In this paper, we analyze this situation from a game theoretic perspective and model the coexistence of CRNs with heterogeneous spectrum as an evolutionary anti-coordination spectrum-sharing game. We derive the evolutionarily stable strategy (ESS) of the game by proving that it cannot be invaded by a greedy strategy. We also derive the replicator dynamics of the proposed evolutionary game, a mechanism with which players can learn from their payoff outcomes of strategic interactions and modify their strategies at every stage of the game and subsequently converge to ESS. Because all CRNs approach ESS based solely upon the common knowledge payoff observations, the evolutionary game can be implemented in a distributed manner. Finally, we analyze the game from the perspective of fairness using Jain\u27s fairness index under selfish behavior from CRNs. Copyright © 2016 John Wiley & Sons, Ltd

Learning under limited information

Abstract We study how human subjects learn under extremely limited information. We use Chen's (forthcoming) data on cost sharing games, and Van Huyck, Battalio and Rankin's (1996) data on coordination games to compare three payoff-based learning models. Under the serial mechanism and coordination games, the payoff-assessment learning mode

Multinational Corporations, Stackelberg Leadership, and Tariff-jumping

In this paper we consider the traditional entry mode choice of an incumbent monopolist facing entry by a single foreign firm. By allowing entry to be either via exporting or foreign direct investment and for the possibility of Stackelberg equilibria where firms can set quantities in one of two time periods, namely "early" or "late," we find conditions where both Cournot and Stackelberg equilibria emerge endogenously. Furthermore, by introducing a simple linear tariff, we see that it not only affects the choice of exporting and FDI in a nonlinear way, but that it can also affect the type of equilibrium that emerges. Copyright � 2006 The Author; Journal compilation � 2006 Blackwell Publishing Ltd.