Hawk-Dove Game Based Interactive Design to Manage Customer Expectation

In the era of experience economy, how best to deliver memorable and exciting customer experiences has become a key issue for service providers, and customers can involve themselves in service experience delivery by actively deciding appropriate services rather than passively accepting existing ones. However, service providers frequently consider profit and cost first despite knowing that high-quality service can maximize satisfaction. A dilemma emerges particularly in the oligopoly market. Oligopoly service providers generally have no need to expend additional efforts and costs in attracting customers, and thus are considered a value-bounded context for customers in which providers only provide customers with existing services and restricted values. Accordingly, this study devises an interaction design mechanism to assist oligopoly service providers in effectively managing customer expectations within the dynamic interactions even in value-bounded contexts. The proposed mechanism models this interaction design problem as a series of Hawk-Dove games toward an evolutionary stable state. The simulation results suggest that oligopoly service providers should change their original mindset and design the interactions to manage customer expectations within the service experience delivery to not only achieve high satisfaction and profit but also engage customers to co-create the values

The Origin of Power in the Need to Cooperate: Parallels Between Political and Economic Power

In this essay, I argue that the power structures in both states and firms should be the same—for example, if state authorities are chosen via a democratic process, the same should hold for authorities in firms. This is because the source of power is the same in both realms, namely, economic and political power derives from its ability to facilitate cooperation. Hence, there is no plausible reason to defend a different power structure for states and firms. To argue this, I start in Chapter 1 by arguing against the most common theory of state power, which is that it derives from the state’s monopoly on violent force. In Chapter 2, I explain R. H. Coase’s theory of corporations, which will serve as partial inspiration for my own theory of political and economic power. Then I describe coordination games and focal points, key concepts that elucidate how people cooperate, in Chapter 3. Chapter 4 contains my main argument that political and economic power arise from the same source, the need for people to cooperate and the ability of power to facilitate this cooperation. I consider four attempts to distinguish states and firms in Chapter 5, and then conclude with some thoughts about the applications of my argument

Bayesian Network Games

This thesis builds from the realization that Bayesian Nash equilibria are the natural definition of optimal behavior in a network of distributed autonomous agents. Game equilibria are often behavior models of competing rational agents that take actions that are strategic reactions to the predicted actions of other players. In autonomous systems however, equilibria are used as models of optimal behavior for a different reason: Agents are forced to play strategically against inherent uncertainty. While it may be that agents have conflicting intentions, more often than not, their goals are aligned. However, barring unreasonable accuracy of environmental information and unjustifiable levels of coordination, they still can\u27t be sure of what the actions of other agents will be. Agents have to focus their strategic reasoning on what they believe the information available to other agents is, how they think other agents will respond to this hypothetical information, and choose what they deem to be their best response to these uncertain estimates. If agents model the behavior of each other as equally strategic, the optimal response of the network as a whole is a Bayesian Nash equilibrium. We say that the agents are playing a Bayesian network game when they repeatedly act according to a stage Bayesian Nash equilibrium and receive information from their neighbors in the network. The first part of the thesis is concerned with the development and analysis of algorithms that agents can use to compute their equilibrium actions in a game of incomplete information with repeated interactions over a network. In this regard, the burden of computing a Bayesian Nash equilibrium in repeated games is, in general, overwhelming. This thesis shows that actions are computable in the particular case when the local information that agents receive follows a Gaussian distribution and the game\u27s payoff is represented by a utility function that is quadratic in the actions of all agents and an unknown parameter. This solution comes in the form of the Quadratic Network Game filter that agents can run locally, i.e., without access to all private signals, to compute their equilibrium actions. For the more generic payoff case of Bayesian potential games, i.e., payoffs represented by a potential function that depends on population actions and an unknown state of the world, distributed versions of fictitious play that converge to Nash equilibrium with identical beliefs on the state are derived. This algorithm highlights the fact that in order to determine optimal actions there are two problems that have to be solved: (i) Construction of a belief on the state of the world and the actions of other agents. (ii) Determination of optimal responses to the acquired beliefs. In the case of symmetric and strictly supermodular games, i.e., games with coordination incentives, the thesis also derives qualitative properties of Bayesian network games played in the time limit. In particular, we ask whether agents that play and observe equilibrium actions are able to coordinate on an action and learn about others\u27 behavior from only observing peers\u27 actions. The analysis described here shows that agents eventually coordinate on a consensus action. The second part of this thesis considers the application of the algorithms developed in the first part to the analysis of energy markets. Consumer demand profiles and fluctuating renewable power generation are two main sources of uncertainty in matching demand and supply in an energy market. We propose a model of the electricity market that captures the uncertainties on both, the operator and the user side. The system operator (SO) implements a temporal linear pricing strategy that depends on real-time demand and renewable generation in the considered period combining Real-Time Pricing with Time-of-Use Pricing. The announced pricing strategy sets up a noncooperative game of incomplete information among the users with heterogeneous but correlated consumption preferences. An explicit characterization of the optimal user behavior using the Bayesian Nash equilibrium solution concept is derived. This explicit characterization allows the SO to derive pricing policies that influence demand to serve practical objectives such as minimizing peak-to-average ratio or attaining a desired rate of return. Numerical experiments show that the pricing policies yield close to optimal welfare values while improving these practical objectives. We then analyze the sensitivity of the proposed pricing schemes to user behavior and information exchange models. Selfish, altruistic and welfare maximizing user behavior models are considered. Furthermore, information exchange models in which users only have private information, communicate or receive broadcasted information are considered. For each pair of behavior and information exchange models, rational price anticipating consumption strategy is characterized. In all of the information exchange models, equilibrium actions can be computed using the Quadratic Network Game filter. Further experiments reveal that communication model is beneficial for the expected aggregate payoff while it does not affect the expected net revenue of the system operator. Moreover, additional information to the users reduces the variance of total consumption among runs, increasing the accuracy of demand predictions

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Learning plan networks in conversational video games

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2007.Includes bibliographical references (p. 121-123).We look forward to a future where robots collaborate with humans in the home and workplace, and virtual agents collaborate with humans in games and training simulations. A representation of common ground for everyday scenarios is essential for these agents if they are to be effective collaborators and communicators. Effective collaborators can infer a partner's goals and predict future actions. Effective communicators can infer the meaning of utterances based on semantic context. This thesis introduces a computational cognitive model of common ground called a Plan Network. A Plan Network is a statistical model that provides representations of social roles, object affordances, and expected patterns of behavior and language. I describe a methodology for unsupervised learning of a Plan Network using a multiplayer video game, visualization of this network, and evaluation of the learned model with respect to human judgment of typical behavior. Specifically, I describe learning the Restaurant Plan Network from data collected from over 5,000 players of an online game called The Restaurant Game.by Jeffrey David Orkin.S.M

The lived experience of the strategic leader: what effective CEOS do, how they do it and an exploration into how they think about it

The purpose of this research was to study the lived experience of being a strategic leader, described as the black box of leadership, and to extend the limited research in this field. The researcher utilised the qualitative ethnographic methodology of direct observation, observing 138 discrete critical incidents that made up the lived experience of the five strategic leaders in the sample. The researcher further utilised observation tools from the field of Neuro Linguistic Programming, personal experiences, metaphors, allegories, analogies as well as deep personal introspection to make sense of the lived experience of the five CEOs. The primary research objective was to answer the question: What do CEOs do and how do they do it? A further related objective was to explore how they think about what they do. The research answered these questions by prising open the 'black box' of the lived experience of the strategic leader. The result of the research is the pure leadership spider web model. The pure leadership spider web model breaks down the lived experience of the strategic leader, the content of the black box, into eight dimensions: the pillars that make up the personal leadership philosophy; emotional states of mind brought to bear in meetings; kinaesthetic patterns used during meetings; meeting dynamics; emotional states brought to bear on day-to-day shop-floor engagement; emotional states brought to bear on leadership engagement sessions with other like business leaders; frames of mind governing the day-to-day experiences; and The Magic Language Box.Business Management and EntrDB

Discourses on social software

Can computer scientists contribute to the solution of societal problems? Can logic help to model social interactions? Are there recipes for making groups with diverging preferences arrive at reasonable decisions? Why is common knowledge important for social interaction? Does the rational pursuit of individual interests put the public interest in danger, and if so, why? Discourses on Social Software sheds light on these and similar questions. This book offers the reader an ideal introduction to the exciting new field of social software. It shows in detail the many ways in which the seemingly abstract sciences of logic and computer science can be put to use to analyse and solve contemporary social problems. The unusual format of a series of discussions among a logician, a computer scientist, a philosopher and some researchers from other disciplines encourages the reader to develop his own point of view. The only requirements for reading this book are a nodding familiarity with logic, a curious mind, and a taste for spicy debate.Kunnen de computerwetenschappers bijdragen aan een oplossing van sociale problemen? Kan logica gebruikt worden om sociale interactie te modelleren? Zijn er regels op te stellen om groepen met afwijkende voorkeuren tot redelijke besluiten te laten komen? Discourses on Social Software biedt de lezer een ideale inleiding op (nog nieuwe) gebied van sociale software. Het toont in detail de vele manieren waarin de schijnbaar abstracte wetenschappen van logica en computerwetenschap aan het werk kunnen worden gezet om eigentijdse sociale problemen te analyseren en op te lossen. Door de ongebruikelijke aanpak in dit boek, namelijk door discussies tussen een logicus, een computerwetenschapper, een filosoof en onderzoekers uit andere disciplines, wordt de lezer aangemoedigd zijn eigen standpunt te ontwikkelen. De enige vereisten om dit boek te lezen zijn enige vertrouwdheid met de logica, een nieuwsgierige geest, en liefde voor een pittig debat

Leadership as role and relationship in social dynamics: An exploratory study seeking a leadership archetype

Previous research on leadership as archetype considered archetype as metaphor and not as it is understood in other literatures as a collectively accepted and defined role within and across cultures. Archetypical theories are posited as useful because they help us understand universal aspects of human behavior; however, empirical research demonstrating archetypical thinking and behavior remains rare. Accordingly, this phenomenological study investigated whether a leadership archetype exists as a shared cognitive template and if so, what characteristics define it. The theoretical framework used to examine the phenomenon of leadership combined leadership theory, philosophy of the mind, Jungian psychology, social constructionist theory, and neuro-linguistic programming. Data were collected in semi-structured interviews from a convenience sample of 10 Midwestern subjects belonging to professional and social organizations and having an expressed interest in leadership. Interviews were coded and sequentially analyzed using a semiotic--phenomenological method that included thematic descriptions, reduction, and interpretation. Results failed to identify an archetypical view of a leader, but identified choice and attribution as key elements in selecting leaders and accepting their leadership. These findings suggested an explanation of leadership as a group consensus that emerges through a dynamic process rather than solely from leader behavior. Implications for positive social change result from the study\u27s contribution toward further understanding of the psychology of leader selection and follower behavior. Given the multiplicity of existing leadership models, the insights gained from this research contribute to the scholarly literature highlighting group-dynamic influences and can lead to improvements in leadership training and leadership development outcomes