Multiagent learning (MAL) is the study of agents learning while in the presence of other agents who are also learning. As a field, MAL is built upon work done in both artificial intelligence and game theory. Game theory has mostly focused on proving that certain theoretical properties hold for a wide class of learning situations while ignoring computational issues, whereas artificial intelligence has mainly focused on designing practical multiagent learning algorithms for small classes of games. This thesis is concerned with finding a balance between the game-theory and artificial-intelligence approaches. We introduce a new learning algorithm, FRAME, which provably converges to the set of Nash equilibria in self-play, while consulting experts which can greatly improve the convergence rate to the set of equilibria. Even if the experts are not well suited to the learning problem, or are hostile, then FRAME will still provably converge. Our second contribution takes this idea further by allowing agents to consult multiple experts, and dynamically adapting so that the best expert for the given game is consulted. The result is a flexible algorithm capable of dealing with new and unknown games. Experimental results validate our approach.

Multiagent learning (MAL) is the study of agents learning while in the presence of other agents who are also learning. As a field, MAL is built upon work done in both artificial intelligence and game theory. Game theory has mostly focused on proving that certain theoretical properties hold for a wide class of learning situations while ignoring computational issues, whereas artificial intelligence has mainly focused on designing practical multiagent learning algorithms for small classes of games. This thesis is concerned with finding a balance between the game-theory and artificial-intelligence approaches. We introduce a new learning algorithm, FRAME, which provably converges to the set of Nash equilibria in self-play, while consulting experts which can greatly improve the convergence rate to the set of equilibria. Even if the experts are not well suited to the learning problem, or are hostile, then FRAME will still provably converge. Our second contribution takes this idea further by allowing agents to consult multiple experts, and dynamically adapting so that the best expert for the given game is consulted. The result is a flexible algorithm capable of dealing with new and unknown games. Experimental results validate our approach.

Although project portfolio management (PPM) has been an active research area over the past 50 years, budget allocation models that consider competition are sparse. Firms faced with the project portfolio management problem must not only consider their current projections for the returns from their projects’ target markets, but must also anticipate that these returns can depend significantly on the investment decisions made by their competitors. In this thesis, we develop four Competitive PPM (CPPM) models wherein firms allocate resources between multiple projects and project returns are influenced by the actions taken by competitors. In the first two CPPM problems, we assume all-or-nothing project investment decisions where firms fully commit to either a project targeting a mature or an emerging market and the investment amount is fixed (first model) or a decision variable (second model). In the final two CPPM problems, firms have a fixed budget which they allocate in a continuous manner between two markets (third model) or multiple markets (fourth model). The returns each firm obtains from investments into these markets are assumed to follow an s-shaped curve (first model), the Inada (1963) conditions (third model), or are determined based on linear demand functions (second and fourth model). In the first model, two competing firms consider investing into two separate projects targeting a mature and an emerging market. We assume that firms have symmetric investment opportunities for each market and each firm simultaneously decides whether to invest in the mature or the emerging market. The returns from these markets are assumed to follow an s-shaped curve and depend on both firms’ investment decision. We characterize the variety of interactions that may emerge in symmetric environments (e.g., Prisoner’s Dilemma or Game of Chicken). For each game, we outline the CPPM strategy that can offer higher returns by exploiting first-mover advantages, cooperation opportunities and aggressive choices. We also discuss the market conditions that lead to these games. In the second model, a similar CPPM setting is considered where two symmetric firms face two target markets. However, we assume that demand for the emerging market is uncertain and may expand through firms’ market entry (positive diffusion effects), while demand for the mature market is known with certainty and cannot expand. Firms decide when to invest, in which market to invest, and how much to invest into this market. Our analysis reveals that the existence of multiple investment opportunities may induce firms to delay their investment even in the absence of demand uncertainty, and that high diffusion effects coupled with low demand uncertainty can drive firms to invest early even if both firms could increase returns by delaying their investment. We then study the asymmetric case where firms differ with respect to their costs and diffusion effects and show some counter-intuitive results. In the third CPPM problem, we consider continuous budget allocations and prove that while a monopoly firm bases its budget allocation decision solely on the marginal returns of the two markets, duopoly firms also account for their average returns from the two markets. This drives duopoly firms, in particular the firm with the smaller budget, to invest more heavily into the mature market. We show that as a firm’s budget increases, the share of its budget that is invested into the mature market decreases while its competitor’s investment into the mature market increases. This chapter also explores how changes to the market parameters and market uncertainty affect the resource allocation decision of firms under competition. Considering the special case of identical budgets, we prove that as the number of competing firms increases (with a fixed total budget), firms allocate an even greater share of their budget into the mature market. The fourth model considers a general case where a number of budget-constrained firms engage in production decisions for multiple markets under competition. Each firm decides how much to produce for each market, subject to its budget constraint. We prove that firms produce greater quantities for markets with higher than average base demand and that these quantities are increasing in the number of competitors (assuming identical production capacities). With asymmetric production capacities, we numerically illustrate how firms with large production capacities may, instead, increase production into lower than average base demand markets. Furthermore, we characterize the increase in return firms can expect from budget increases and conjecture that if some markets are not served by all firms, the remaining firms reduce their production into those markets where some firms are not producing.

We study the supply chain with two manufacturers producing competing products and selling them through a common retailer. The two manufacturers must decide on the wholesale price and the level of service they plan to provide to the consumer. Each firm are assumed to optimize only its own profit (uncoordinated). The consumer demand depends on two factors: (1) retail price, and (2) service level provided by the manufacturer. We extend the study on this basic model in three directions. First, we explore the role of bargaining power in supply chain strategic interactions. We derive and compare equilibrium solutions for the supply chain under three different scenarios (e.g., Manufacturer Stackelberg, Retailer Stackelberg, and Vertical Nash). Second, we extend the framework to study multi-period model. In this model, demand also depends on the past period retail prices and service levels, as well as current prices and service levels. Game-theoretic approaches and dynamic system and control theory are used as tools to model the problem. Finally, we examine a single period problem with stochastic demand. When demand is uncertain, the retailer faces a newsvendor-type problem. In our model, the newsvendor must manage two competing products against a price-dependent demand. We derive an expression for the newsvendor's optimal retail prices. Next, we provide an algorithm to search for the equilibrium wholesale price and service level, given that the manufacturers know the retailer's reaction function. Some numerical examples are provided.

The theme of the thesis is a verifying framework for balanced scorecard or multi ¡V dimensional managerial indicators . The verifying framework consists of seven major modules , TETRAD , and LISREL . The seven major modules are mission-strategy-module,factor-analysis-module , dimension-indicator-module,principle-component- analysis-module,canonical-correlation-module, game-theory-module,and performance-vector-model. The verifying framework takes advantage of knowledge or tools of vector analysis, multivariate statistical analysis , game theory , fuzzy sets , and multiobjective decision making . The thesis hopes to offer a preciser quantitative verifying framework for balanced scorecard or multi-dimensional managerial indicators .

The development of online game in Taiwan is matured and there are also many derived products about it. The total output value of online game industry is still growing at present and the growing rate is at about 8.5% in 2010. There are still many issues worthy to be discussed at the same time. First of all, this research classified the virtual products of online game. Secondly, by analyzing the documents about network externality, the research discussed the effect that those products could influence online game players. Next, with the game model which constructed in the research, the interaction of ¡§paid-player¡¨, ¡§free player¡¨ and ¡§potential player¡¨ were discussed. Finally, the research also analyzed the consumer behavior under different situation of network externality and gave some pricing reference for online game firms. There is the conclusion of the research. To maximum the profit, the online game firms must set higher price for those virtual products which may have negative effects compared to those products which do not have negative effects.

Thesis (M.S.)--University of Wyoming, 2008. / Title from PDF title page (viewed on Dec. 4, 2009). Includes bibliographical references (p. 45-47).

Thesis (Ph. D.)--Florida State University, 2004. / Advisor: Dr. Craig A. Nolder, Florida State University, College of Arts and Sciences, Dept. of Mathematics. Title and description from dissertation home page (viewed June 29, 2004). Includes bibliographical references.

Thesis (M.Phil.)--City University of Hong Kong, 2009. / "Submitted to Department of Manufacturing Engineering and Engineering Management in partial fulfillment of the requirements for the degree of Master of Philosophy." Includes bibliographical references (leaves 87-91)

Thesis (Ph. D.)--Lehigh University, 2003. / Includes vita. Includes bibliographical references (leaves 241-252).