Empirical Inference of Numerical Information into Causal Strategy Models by Means of Artificial Intelligence

Empirical Inference of Numerical Information into Causal Strategy Models by Means of Artificial Intelligence

Christian Hillbrand (University of Liechtenstein, Principality of Liechtenstein)
DOI: 10.4018/978-1-60566-144-5.ch016
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The motivation for this chapter is the observation that many companies build their strategy upon poorly validated hypotheses about cause and effect of certain business variables. However, the soundness of these cause-and-effect-relations as well as the knowledge of the approximate shape of the functional dependencies underlying these associations turns out to be the biggest issue for the quality of the results of decision supporting procedures. Since it is sufficiently clear that mere correlation of time series is not suitable to prove the causality of two business concepts, there seems to be a rather dogmatic perception of the inadmissibility of empirical validation mechanisms for causal models within the field of strategic management as well as management science. However, one can find proven causality techniques in other sciences like econometrics, mechanics, neuroscience, or philosophy. Therefore this chapter presents an approach which applies a combination of well-established statistical causal proofing methods to strategy models in order to validate them. These validated causal strategy models are then used as the basis for approximating the functional form of causal dependencies by the means of Artificial Neural Networks. This in turn can be employed to build an approximate simulation or forecasting model of the strategic system.
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Planning and implementing corporate strategy very often requires substantial efforts in gathering relevant data and information underlying the decisions to be met. Hence, the decision makers face at least two elementary issues: First, the planner has to be supplied with appropriate data about the underlying relevant key figures and business drivers as well as environmental information related to the market or competitors. This first function of data support as outlined before is the main focus of so-called management information systems (MIS). These tools usually employ powerful techniques to gather the necessary figures as a basis for strategic planning efforts.

Second, this raw data has to be arranged within decision models in order to reduce the variety and complexity coming with it: One characteristic of a complex strategic decision is that it is influenced by an immense set of business variables which have to be analyzed in this context. As a consequence data supporting tools do not provide appropriate aids for this type of entrepreneurial function: It is to reduce the complexity emerging from this amount of data which becomes the principal task of decision support systems (DSS). Hence it can be observed that the architecture of any arbitrary DSS is highly dependent of the managerial approach it is designed to support. It necessarily incorporates the notion of a mental model underlying the respective decision theory as well as techniques to derive decisions from these assumptions. Sprague & Carlson (1982) specify these two core components of a DSS as model base and method base, respectively. The former defines the structure of the decision model which arranges the raw data provided by a data support component, whereas the latter encompasses decision theoretic methods specifically designed to operate on the given decision model. According to the type of the model base, analytic techniques like optimization as well as statistical methods or stochastic approaches like simulation are used to draw decisions from the raw data organized in the decision model.

The rest of the chapter is organized as follows: The following section provides review of the appropriate literature within the field of causal strategy planning techniques as well as of causality concepts. Consequently, specific causality criteria are defined on this basis. This definition is employed in the subsequent section in order to establish an approach for the automated proof of nomothetic cause-and-effect hypotheses. Since every single of these proven causal relations are characterized by an arbitrary unknown cause-and-effect function, this function has to be approximated in order to build a quantitative model base for DSSs. Therefore this chapter discusses appropriate approximation techniques and proposes a nonparametric approach for the universal approximation of arbitrary cause-and-effect functions by the means of ANNs. This chapter is concluded by the presentation of experimental results.

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Table of Contents
Vijayan Sugumaran
Chapter 1
Hong Lin
In this chapter a program construction method based on ?-Calculus is proposed. The problem to be solved is specified by first-order predicate logic... Sample PDF
Designing Multi-Agent Systems from Logic Specifications: A Case Study
Chapter 2
Rahul Singh
Organizations use knowledge-driven systems to deliver problem-specific knowledge over Internet-based distributed platforms to decision-makers.... Sample PDF
Multi-Agent Architecture for Knowledge-Driven Decision Support
Chapter 3
Farid Meziane
Trust is widely recognized as an essential factor for the continual development of business-to-customer (B2C) electronic commerce (EC). Many trust... Sample PDF
A Decision Support System for Trust Formalization
Chapter 4
Mehdi Yousfi-Monod
The work described in this chapter tackles learning and communication between cognitive artificial agents and trying to meet the following issue: Is... Sample PDF
Using Misunderstanding and Discussion in Dialog as a Knowledge Acquisition or Enhancement Procecss
Chapter 5
Sungchul Hong
In this chapter, we present a two-tier supply chain composed of multiple buyers and multiple suppliers. We have studied the mechanism to match... Sample PDF
Improving E-Trade Auction Volume by Consortium
Chapter 6
Manoj A. Thomas, Victoria Y. Yoon, Richard Redmond
Different FIPA-compliant agent development platforms are available for developing multiagent systems. FIPA compliance ensures interoperability among... Sample PDF
Extending Loosely Coupled Federated Information Systems Using Agent Technology
Chapter 7
H. Hamidi
The reliable execution of mobile agents is a very important design issue in building mobile agent systems and many fault-tolerant schemes have been... Sample PDF
Modeling Fault Tolerant and Secure Mobile Agent Execution in Distributed Systems
Chapter 8
Xiannong Meng, Song Xing
This chapter reports the results of a project attempting to assess the performance of a few major search engines from various perspectives. The... Sample PDF
Search Engine Performance Comparisons
Chapter 9
Antonio Picariello
Information retrieval can take great advantages and improvements considering users’ feedbacks. Therefore, the user dimension is a relevant component... Sample PDF
A User-Centered Approach for Information Retrieval
Chapter 10
Aboul Ella Hassanien, Jafar M. Ali
This chapter presents an efficient algorithm to classify and retrieve images from large databases in the context of rough set theory. Color and... Sample PDF
Classification and Retrieval of Images from Databases Using Rough Set Theory
Chapter 11
Lars Werner
Text documents stored in information systems usually consist of more information than the pure concatenation of words, i.e., they also contain... Sample PDF
Supporting Text Retrieval by Typographical Term Weighting
Chapter 12
Ben Choi
Web mining aims for searching, organizing, and extracting information on the Web and search engines focus on searching. The next stage of Web mining... Sample PDF
Web Mining by Automatically Organizing Web Pages into Categories
Chapter 13
John Goh
Mobile user data mining is about extracting knowledge from raw data collected from mobile users. There have been a few approaches developed, such as... Sample PDF
Mining Matrix Pattern from Mobile Users
Chapter 14
Salvatore T. March, Gove N. Allen
Active information systems participate in the operation and management of business organizations. They create conceptual objects that represent... Sample PDF
Conceptual Modeling of Events for Active Information Systems
Chapter 15
John M. Artz
Earlier work in the philosophical foundations of information modeling identified four key concepts in which philosophical groundwork must be further... Sample PDF
Information Modeling and the Problem of Universals
Chapter 16
Christian Hillbrand
The motivation for this chapter is the observation that many companies build their strategy upon poorly validated hypotheses about cause and effect... Sample PDF
Empirical Inference of Numerical Information into Causal Strategy Models by Means of Artificial Intelligence
Chapter 17
Yongjian Fu
In this chapter, we propose to use N-gram models for improving Web navigation for mobile users. Ngram models are built from Web server logs to learn... Sample PDF
Improving Mobile Web Navigation Using N-Grams Prediction Models
Chapter 18
Réal Carbonneau, Rustam Vahidov, Kevin Laframboise
Managing supply chains in today’s complex, dynamic, and uncertain environment is one of the key challenges affecting the success of the businesses.... Sample PDF
Forecasting Supply Chain Demand Using Machine Learning Algorithms
Chapter 19
Teemu Tynjala
The present study implements a generic methodology for describing and analyzing demand supply networks (i.e. networks from a company’s suppliers... Sample PDF
Supporting Demand Supply Network Optimization with Petri Nets
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