scholarly journals Advertising and Competition for Market Share between a New Good Producer and a Remanufacturer

2018 ◽  
Vol 19 (1) ◽  
pp. 113-118 ◽  
Author(s):  
Amitrajeet A. Batabyal ◽  
Hamid Beladi
Keyword(s):  
Normal Form ◽  
Market Share ◽  
Strategic Interaction ◽  
Relevant Market ◽  
Advertising Campaign ◽  
Good Producer ◽  
Dominated Strategies ◽  
Form Representation ◽  
Rival Firms ◽  
New Good

Abstract We study the strategic interaction between a new good producer and a remanufacturer who use advertising campaigns to compete for a dominant share of the market for a certain good. Each firm chooses one of three possible strategies for running its advertising campaign. The two rival firms care only about capturing a dominant share of the relevant market. Hence, if a firm expects to capture dominant market share with probability p є [0, 1], then its payoff in the game we study is also p. Our analysis leads to four results. First, we provide the normal form representation of the game between the new good producer and the remanufacturer. Second, we specify the game in matrix form. Third, we indicate what happens at each stage of the elimination of strictly dominated strategies. Finally, we show that the iterated elimination of strictly dominated strategies yields a clear and unique prediction about the outcome of the advertising game.

Part II Vertical Agreements Under Regulation 330/2010, 5 Article 3: Market Share Threshold

2018 ◽  
Author(s):  
Wijckmans Frank ◽  
Tuytschaever Filip
Keyword(s):  
Market Share ◽  
Practical Application ◽  
Relevant Market ◽  
Market Shares ◽  
Definition Of ◽  
Over Time

This chapter discusses the market share limits that determine the applicability of Regulation 330/2010. Each of the supplier and the buyer must in principle remain below an individual limit of 30 per cent. In order to assess the market share limits, the chapter addresses the following steps of the analysis: (i) ninth step: definition of the relevant market; and (ii) tenth step: calculation of the market shares. It concludes by addressing the concrete and practical application of the market share limits in accordance with Regulation 330/2010 and offers easy-to-read overview tables illustrating the effect of changes in the market share levels over time.

scholarly journals LTL and Beyond: Formal Languages for Reward Function Specification in Reinforcement Learning

2019 ◽  
Author(s):  
Alberto Camacho ◽  
Rodrigo Toro Icarte ◽  
Toryn Q. Klassen ◽  
Richard Valenzano ◽  
Sheila A. McIlraith
Keyword(s):  
Reinforcement Learning ◽  
Normal Form ◽  
State Of The Art ◽  
Formal Languages ◽  
Function Structure ◽  
Q Learning ◽  
Reward Function ◽  
Form Representation ◽  
Reward Shaping ◽  
Reward Functions

In Reinforcement Learning (RL), an agent is guided by the rewards it receives from the reward function. Unfortunately, it may take many interactions with the environment to learn from sparse rewards, and it can be challenging to specify reward functions that reflect complex reward-worthy behavior. We propose using reward machines (RMs), which are automata-based representations that expose reward function structure, as a normal form representation for reward functions. We show how specifications of reward in various formal languages, including LTL and other regular languages, can be automatically translated into RMs, easing the burden of complex reward function specification. We then show how the exposed structure of the reward function can be exploited by tailored q-learning algorithms and automated reward shaping techniques in order to improve the sample efficiency of reinforcement learning methods. Experiments show that these RM-tailored techniques significantly outperform state-of-the-art (deep) RL algorithms, solving problems that otherwise cannot reasonably be solved by existing approaches.

Normal form representation of the aeroelastic response of the Goland wing

2011 ◽  
Vol 67 (3) ◽  
pp. 1847-1861 ◽  
Author(s):  
Ali H. Nayfeh ◽  
Mehdi Ghommem ◽  
Muhammad R. Hajj
Keyword(s):  
Normal Form ◽  
Aeroelastic Response ◽  
Form Representation

Modeling competitive situations

2020 ◽  
pp. 355-386
Author(s):  
David M. Kreps
Keyword(s):  
Normal Form ◽  
Noncooperative Game ◽  
Extensive Form ◽  
Game Tree ◽  
Decision Node ◽  
Form Game ◽  
Normal Form Game ◽  
Form Representation ◽  
Information Sets ◽  
Extensive Form Game

This chapter studies how competitive situations are conventionally modeled in noncooperative game theory. It uses two sorts or forms of models: the so-called extensive form game and the normal or strategic form game. An extensive form representation of a noncooperative game is composed of the following list of items: a list of players; a game tree; an assignment of decision nodes to players or to nature; lists of actions available at each decision node and a correspondence between immediate successors of each decision node and available actions; information sets; an assignment of payoffs for each player to terminal nodes; and probability assessments over the initial nodes and over the actions at any node that is assigned to nature. There is no single way to proceed in general from a normal form game to a corresponding extensive form game. In one obvious extensive form, the players all choose complete strategies simultaneously, but often other extensive forms could be constructed from a given normal form.

Correlates of successful brand advertising in China

2007 ◽  
Vol 19 (1) ◽  
pp. 40-56 ◽  
Author(s):  
Susan H.C. Tai
Keyword(s):  
Correlation Analysis ◽  
Market Share ◽  
Market Research ◽  
Success Factors ◽  
Response Rate ◽  
Media Selection ◽  
Advertising Campaign ◽  
Content Type ◽  
Brand Advertising ◽  
The Relationship

PurposeThis study aims to investigate the relationship between successful brand advertising campaigns in China and various factors such as message/creativity, media selection, market research, competition, market share, product uniqueness, and agency/client relationships.Design/methodology/approachThe data for the study were collected by mailing a questionnaire to 283 advertising agencies executives who were selected from the All‐Asia Ad Agency Guide. A total of 1,086 questionnaires were sent out and 163 were returned for a response rate of 15 per cent. Factor analysis was first used to identify various success factors, and ANOVA was used to compare the means of each factor related to the degree of success of the campaign. Correlation analysis was then used to examine the relationship between successful brand advertising campaign and various success factors.FindingsThe results of the ANOVA indicate that there are significant relationships between some items in each factor and the degree of success of an advertising campaign. Correlation analysis further reveals that message/creativity, media selection, market research, market share, and product uniqueness are significantly related to the success of brand advertising in China. No significant relationship is found between brand success and competition or agency/client relationship.Research limitations/implicationsThe respondents may have been biased about the extent to which their advertising campaigns are successful or how creative an advertisement should be. Their perceptions of successful or creative advertising could be very different, especially in relation to those questions that asked respondents to critique their own work. All of these affect the rigor of the study. Another limitation of the study is the low response rate. If the sample size had been large enough, comparisons could have been made concerning the correlates of successful brand advertising across different regions in China.Originality/valueIn addition to providing researchers with further understanding of brand advertising in China, this study provides some insights about the ways in which multinational advertising managers contribute to successful brand advertising.

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