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Pure Strategies (pure + strategy)

Distribution by Scientific Domains
Business, Economics, Finance and Accounting100%

Terms modified by Pure Strategies

  • pure strategy equilibrium
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    Selected Abstracts

    Price Dispersion and Consumer Reservation Prices

    JOURNAL OF ECONOMICS & MANAGEMENT STRATEGY, Issue 1 2005
    Simon P. Anderson
    We describe firm pricing when consumers follow simple reservation price rules. In stark contrast to other models in the literature, this approach yields price dispersion in pure strategies even when firms have the same marginal costs. At the equilibrium, lower price firms earn higher profits. The range of price dispersion increases with the number of firms: the highest price is the monopoly price, while the lowest price tends to marginal cost. The average transaction price remains substantially above marginal cost even with many firms. The equilibrium pricing pattern is the same when prices are chosen sequentially. [source]

    HOTELLING'S BEACH WITH LINEAR AND QUADRATIC TRANSPORTATION COSTS: EXISTENCE OF PURE STRATEGY EQUILIBRIA,

    AUSTRALIAN ECONOMIC PAPERS, Issue 1 2007
    ALAIN EGLI
    In Hotelling type models consumers have the same transportation cost function. We deviate from this assumption and introduce two consumer types. Some consumers have linear transportation costs, while the others have quadratic transportation costs. If at most half the consumers have linear transportation costs, a subgame perfect equilibrium in pure strategies exists for all symmetric locations. Furthermore, no general principle of differentiation holds. With two consumer types, the equilibrium pattern ranges from maximum to intermediate differentiation. The degree of product differentiation depends on the fraction of consumer types. [source]

    Reverse Auctions with Multiple Reinforcement Learning Agents,

    DECISION SCIENCES, Issue 1 2008
    Subhajyoti Bandyopadhyay
    ABSTRACT Reverse auctions in business-to-business (B2B) exchanges provide numerous benefits to participants. Arguably the most notable benefit is that of lowered prices driven by increased competition in such auctions. The competition between sellers in reverse auctions has been analyzed using a game-theoretic framework and equilibria have been established for several scenarios. One finding of note is that, in a setting in which sellers can meet total demand with the highest-bidding seller being able to sell only a fraction of the total capacity, the sellers resort to a mixed-strategy equilibrium. Although price randomization in industrial bidding is an accepted norm, one might argue that in reality managers do not utilize advanced game theory calculations in placing bids. More likely, managers adopt simple learning strategies. In this situation, it remains an open question as to whether the bid prices converge to the theoretical equilibrium over time. To address this question, we model reverse-auction bidding behavior by artificial agents as both two-player and n -player games in a simulation environment. The agents begin the game with a minimal understanding of the environment but over time analyze wins and losses for use in determining future bids. To test for convergence, the agents explore the price space and exploit prices where profits are higher, given varying cost and capacity scenarios. In the two-player case, the agents do indeed converge toward the theoretical equilibrium. The n -player case provides results that reinforce our understanding of the theoretical equilibria. These results are promising enough to further consider the use of artificial learning mechanisms in reverse auctions and other electronic market transactions, especially as more sophisticated mechanisms are developed to tackle real-life complexities. We also develop the analytical results when one agent does not behave strategically while the other agent does and show that our simulations for this environment also result in convergence toward the theoretical equilibrium. Because the nature of the best response in the new setting is very different (pure strategy as opposed to mixed), it indicates the robustness of the devised algorithm. The use of artificial agents can also overcome the limitations in rationality demonstrated by human managers. The results thus have interesting implications for designing artificial agents in automating bid responses for large numbers of bids where human intervention might not always be possible. [source]

    Strategic Auditing: An Incomplete Information Model

    JOURNAL OF BUSINESS FINANCE & ACCOUNTING, Issue 5-6 2001
    Peter Cheng
    This paper presents a stylized model of the strategy game between the auditor and the client. The client is assumed to have either good or bad inherent risk in her reporting system. She chooses a reporting effort level to maintain the accounting records and data management depending on her type of inherent risk. The auditor chooses a high or low level of audit procedures. A high level of auditing procedures will reveal the client's type and effort from which the auditor can decide either to qualify the financial statements or to issue a clean report. The client and the auditor are assumed to move simultaneously. Pure strategy equilibria are derived for all the undominated strategies between the auditor and the client in the region of the model that is more similar to the Fellingham and Newman (1985) model. Unlike their model in which a high auditing level is never a pure strategy in equilibrium, we obtain pure strategy equilibria for high auditing levels. [source]