Fuzzy decision making system and the dynamics of business games

Effective and efficient strategic decision making is the backbone for the success of a business organisation among its competitors in a particular industry. The results of these decision making processes determine whether the business will continue to survive or not. In this thesis, fuzzy logic (FL) concepts and game theory are being used to model strategic decision making processes in business organisations. We generally modelled competition by business organisations in industries as games where each business organization is a player. A player formulates his own decisions by making strategic moves based on uncertain information he has gained about the opponents. This information relates to prevailing market demand, cost of production, marketing, consolidation efforts and other business variables. This uncertain information is being modelled using the concept of fuzzy logic. In this thesis, simulation experiments were run and results obtained in six different settings. The first experiment addresses the payoff of the fuzzy player in a typical duopoly system. The second analyses payoff in an n-player game which was used to model a perfect market competition with many players. It is an extension of the two-player game of a duopoly market which we considered in the first experiment. The third experiment used and analysed real data of companies in a case study. Here, we chose the competition between Coca-cola and PepsiCo companies who are major players in the beverage industry. Data were extracted from their published financial statements to validate our experiment. In the fourth experiment, we modelled competition in business networks with uncertain information and varying level of connectivity. We varied the level of interconnections (connectivity) among business units in the business networks and investigated how missing links affect the payoffs of players on the networks. We used the fifth experiment to model business competition as games on boards with possible constraints or restrictions and varying level of connectivity on the boards. We also investigated this for games with uncertain information. We varied the level of interconnections (connectivity) among the nodes on the boards and investigated how these a ect the payoffs of players that played on the boards. We principally used these experiments to investigate how the level of availability of vital infrastructures (such as road networks) in a particular location or region affects profitability of businesses in that particular region. The sixth experiment contains simulations in which we introduced the fuzzy game approach to wage negotiation in managing employers and employees (unions) relationships. The scheme proposes how employers and employees (unions) can successfully manage the deadlocks that usually accompany wage negotiations. In all cases, fuzzy rules are constructed that symbolise various rules and strategic variables that firms take into consideration before taken decisions. The models also include learning procedures that enable the agents to optimize these fuzzy rules and their decision processes. This is the main contribution of the thesis: a set of fuzzy models that include learning, and can be used to improve decision making in business

New structured knowledge network for strategic decision-making in IT innovative and implementable projects

This document is the Accepted Manuscript version of the following article: Ali Alkhuraji, Sahofeng Liu, Festus Oluseyi Oderanti, and Phil Megicks, 'New structured knowledge network for strategic decision-making in IT innovative and implementable projects', Journal of Business Research, Vol. 69 (5): 1534-1538, first published online 28 October 2015. The final published version is available online at https://doi.org/10.1016/j.jbusres.2015.10.012. Copyright © 2015 Elsevier Inc. All rights reserved.This study investigates the development of a structured knowledge network model in information technology (IT) innovative and implementable projects to facilitate knowledge sharing and transfer in a multi-organization context. The study employs a practice-based perspective by using an exploratory case study approach and a combination of thematic analysis and comparative analysis to analyze the data across public organizations, private organizations, and international companies. The results identify organizational factors and their influence on knowledge channels and knowledge networks. The study contributes to organizational, administrative and knowledge management theories regarding organizational strategy, organizational culture, organizational capacity, knowledge network externalities, knowledge network intermediaries, and knowledge network infrastructures.Peer reviewe

Knowledge network modelling to support decision-making for strategic intervention in IT project-oriented change management

This is the Accepted Manuscript version of an article published by Taylor & Francis in Journal of Decision Systems on 20 March 2014, available online: http://www.tandfonline.com/doi/abs/10.1080/12460125.2014.886499.This paper focuses on knowledge management to enhance decision support systems for strategic intervention in information technology (IT) project-oriented change management. It proposes a model of change management knowledge networks (CMKNM) to support decision by tackling three existing issues: insufficient knowledge traceability based on the relationships between knowledge elements and key factors, lack of procedural knowledge to provide adequate policies to guide changes, and lack of ‘lessons learned’ documentation in knowledge bases. A qualitative method was used to investigate issues surrounding knowledge mobilisation and knowledge networks. Empirical study was undertaken with industries to test the CMKNM. Results are presented from the empirical study on the key factors influencing knowledge mobilisation in IT project-oriented change management, knowledge networks and connections. The CMKNM model allows key knowledge mobilisation factors to be aligned with each other; it also defines the connections between knowledge networks allowing knowledge to be mobilised by tracing knowledge channels to support decision.Peer reviewe

Uncertainty in wage increase negotiation and decisions; An approach from flexible fuzzy inference system (FIS)

Festus Oderanti, 'Uncertanty in wage increrase negotiation and decidions; An approach from flexible fuzzy inference system (FIS)' in Arijit Mukherjee Ed., Wages and Employment: Economics, Structure and Gender Differences (New York: Nova Science Publishers Inc., USA. 2013) ISBN: 9781626184220Wage negotiation has always caused persistent problems in different organisations and on many occasions, there have been cases in which the entire workforce of countries embarked on industrial strikes that resulted from wage increase negotiation disputes. The root causes of wage negotiation disputes, in most cases, are often connected to the inability of either of the two parties involved (employers and employees' unions) to sustain or maintain the status quo contained in their earlier agreement on wage increase. With the aid of fuzzy inference system and concepts of game theory, this chapter proposes a flexible scheme for wage increase negotiation and decision problems. For example, rather than specifying rigid 5%yearly increase of wages, we propose that the uncertain factors which are mostly difficult to predict and that could affect wage decisions need to be taken into consideration by the wage formula. These include business revenues or (profit), inflation rate, number of competitors, cost of production, and other uncertain factors that may affect business operations. The accuracy of the fuzzy rule base and the game strategies will help to mitigate the adverse effects that a business may suffer from these uncertain factors. The proposed approach is illustrated with a case study and the procedure and methodology may be easily implemented by business organisations in their wage bargaining and decision processes

Automated game approach to wage negotiation and decision problems

We proposed a profit sharing strategic game approach to wage negotiation and decision problems in business organisations. In the scheme, both the employer and the union choose their strategies and the game is played in five rounds. We refer to our model as automated game approach to wage negotiation and decision problems (AGAW). Our method proposes profit (positive or negative) sharing sequential game approach in modeling wage increase decisions within a firm in a competitive industry and this game is played between the firm's management and the union. The proposed approach is illustrated with a case study. The procedure and methodology proposed in this research may be easily implemented by business organisations in their wage bargaining and decision processes

Dynamics of business games with management of fuzzy rules for decision making

Effective and efficient strategic decision making is the backbone for the success of a business organization. These decision making processes, used among its competitors in a particular industry, determine whether the business will continue to survive or not. In this research, fuzzy logic (FL) concept and game theory are being used to model strategic decision making processes by business organizations. Competition between business organizations is viewed as a game with each business organization as a player. A player formulates his own decisions by making his strategic moves based on uncertain information. This is the information he has about the opponents with respect to prevailing or anticipated market demand, cost of production, marketing, consolidation efforts and some other business variables. This uncertain information is being modelled using the concept of fuzzy logic. The game is played between a fuzzy agent and human agents in a resource allocation game between two players with uncertain information. Moreover, fuzzy rules are constructed that symbolize various rules and strategic variables that a firm takes into consideration before taking a decision. Our model also includes a learning procedure that enables the agent to optimize the fuzzy rules and his decision processes. Matlab software was used for the design and implementation of the fuzzy decision making system and this procedure and methodology can be easily implemented by business managers and can assist them in their strategic policy formulation

Automatic fuzzy decision making system with learning for competing and connected businesses

We study uncertainties surrounding competition on business networks and board games. We investigate these uncertainties using concepts of fuzzy logic and game theory. We investigate how the payoff of the players is affected by a number of factors. These include the level of connectivity or number of links, the number of competitors, possible constraints on the networks and on the boards, as well as choice of strategy adopted by competitors. We introduce one fuzzy player in the game. This player uses fuzzy rules to make strategic decisions. We introduce learning to train and analyze how the fuzzy player adapts over time during the game

Dynamics of business games with management of fuzzy rules for decision making

Effective and efficient strategic decision making is the backbone for the success of a business organization. These decision making processes, used among its competitors in a particular industry, determine whether the business will continue to survive or not. In this research, fuzzy logic (FL) concept and game theory are being used to model strategic decision making processes by business organizations. Competition between business organizations is viewed as a game with each business organization as a player. A player formulates his own decisions by making his strategic moves based on uncertain information. This is the information he has about the opponents with respect to prevailing or anticipated market demand, cost of production, marketing, consolidation efforts and some other business variables. This uncertain information is being modelled using the concept of fuzzy logic. The game is played between a fuzzy agent and human agents in a resource allocation game between two players with uncertain information. Moreover, fuzzy rules are constructed that symbolize various rules and strategic variables that a firm takes into consideration before taking a decision. Our model also includes a learning procedure that enables the agent to optimize the fuzzy rules and his decision processes. Matlab software was used for the design and implementation of the fuzzy decision making system and this procedure and methodology can be easily implemented by business managers and can assist them in their strategic policy formulation.Fuzzy logic Membership functions Business game Game theory Zero sum

Wage increment problems and fuzzy inference approach

We propose a flexible scheme for employers and employees which they can use as a decision support system in their future salary negotiations. This scheme uses a fuzzy inference system for arriving at more mutually agreeable decisions on wage negotiation. For example, rather than specifying 5 yearly increase of wages, we propose that the wage increase formula needs to take into consideration other factors which are mostly difficult to predict with certainty. These include inflation rate, business revenues or (profit), cost of production, number of competitors and other uncertain factors that may affect business operations. The accuracy of the fuzzy rule base will help to mitigate the adverse effects that a business may suffer from these uncertain factors. Based on our scheme, we propose that employers and employees should calculate their future wage by using a fuzzy rule base that takes into consideration those variables which are mostly uncertain and that could affect their decisions

Uncertainty in business decisions and fuzzy games approach

We developed a decision support scheme that could be used and adapted to various business decision processes. These involve decisions that are made under uncertain situations such as business competition in the market or wage negotiation within a firm. The scheme uses game strategies and fuzzy inference concepts to effectively grasp the variables in these uncertain situations. The games are played between human and fuzzy players. The accuracy of the fuzzy rule base and the game strategies help to mitigate the adverse effects that a business may suffer from these uncertain factors We also introduced learning which enables the fuzzy player to adapt over time. We tested this scheme in different scenarios and discover that it could be an invaluable tool in the hand of entrepreneurs that are operating under uncertain and competitive business environments