Connecting adaptive behaviour and expectations in models of innovation: The Potential Role of Artificial Neural Networks

In this methodological work I explore the possibility of explicitly modelling expectations conditioning the R&D decisions of firms. In order to isolate this problem from the controversies of cognitive science, I propose a black box strategy through the concept of “internal model”. The last part of the article uses artificial neural networks to model the expectations of firms in a model of industry dynamics based on Nelson & Winter (1982)

Theoretical optimisation of IT/IS investments: A research note

The justification of Information Technology (IT) is inherently fuzzy, both in theory and practice. The reason for this is due to the largely intangible dimensions of IT projects. In view of this, this research note presents the results of on-going research, in the application of Fuzzy Cognitive Mapping (FCM), as a tool to identify complex functional interrelationships associated with the justification of IT. This paper presents a theoretical functional model which describes these relationships, and by using an FCM, further interrelationships are developed in the context of justifying IT projects. A procedure which would address the optimisation of these intangible relationships in the form of a Genetic Algorithm (GA) is proposed as a process for Investment Justification

Advancing the state of the art in the modelling and simulation of information systems evaluation

It is widely accepted that Information Systems Evaluation (ISE) is a powerful and useful technique that can be used to assess IT/IS investments in an a-priori or a-posteriori sense. Traditional approaches to ISE have tended to centre upon financial and management accounting frameworks, seeking to reconcile tangible and intangible costs, benefits, risks and value factors. Such techniques, however, do not provide the IS researcher or practitioner with further insight or appreciation of any inherent and implicit inter-relationships, in the investment justification process. Thus, this paper outlines and discusses via a taxonomy and resulting classification, alternative and complementary approaches that can be applied to ISE from the fields of Artificial Intelligence (AI), Operational Research (OR) and Management Science (MS). The paper subsequently concludes that such approaches can be potentially used by researchers and practitioners in the field, as a basis for carrying out further research in the field of applied ISE

Enhanced genetic algorithm-based fuzzy multiobjective strategy to multiproduct batch plant design

This paper addresses the problem of the optimal design of batch plants with imprecise demands in product amounts. The design of such plants necessary involves how equipment may be utilized, which means that plant scheduling and production must constitute a basic part of the design problem. Rather than resorting to a traditional probabilistic approach for modeling the imprecision on product demands, this work proposes an alternative treatment by using fuzzy concepts. The design problem is tackled by introducing a new approach based on a multiobjective genetic algorithm, combined wit the fuzzy set theory for computing the objectives as fuzzy quantities. The problem takes into account simultaneous maximization of the fuzzy net present value and of two other performance criteria, i.e. the production delay/advance and a flexibility index. The delay/advance objective is computed by comparing the fuzzy production time for the products to a given fuzzy time horizon, and the flexibility index represents the additional fuzzy production that the plant would be able to produce. The multiobjective optimization provides the Pareto's front which is a set of scenarios that are helpful for guiding the decision's maker in its final choices. About the solution procedure, a genetic algorithm was implemented since it is particularly well-suited to take into account the arithmetic of fuzzy numbers. Furthermore because a genetic algorithm is working on populations of potential solutions, this type of procedure is well adapted for multiobjective optimization

Competing R&D Strategies in an Evolutionary Industry Model

This article aims to test the relevance of learning through Genetic Algorithms, in opposition with fixed R&D rules, in a simplified version of the evolutionary industry model of Nelson and Winter. These two R&D strategies are compared from the points of view of industry performance (welfare) and firms' relative performance (competitive edge): the results of simulations clearly show that learning is a source of technological and social efficiency as well as a mean for market domination.Learning,Innovation, Industry dynamics, Bounded rationality, Learning, Genetic algorithms

A revised perspective on the evaluation of IT/IS investments using an evolutionary approach

On-going research into the evaluation of Information Technology (IT) / Information Systems (IS) projects has shown that aerospace and supply chain industries are needing to address the issue of effective project investment in order to gain technological and competitive advantage. The evaluative nature of the justification process requires a mapping of interrelated quantities to be optimised. Earlier work by the authors (Irani and Sharif 1997) has presented a theoretical functional model that describes these relationships in turn. By applying a fuzzy mapping to these variables, the optimisation of intangible relationships in the form of a Genetic Algorithm (GA) is proposed as a method for investment justification. This paper revises and reviews these key concepts and provides a recapitulation of this optimisation problem in terms of long-term strategy options and cost implications. Glossary of terms : DC = Direct Costs, FA = Financial Appraisal, FR = Financial Risks, FUR = Functional Risks, HC = Human Costs, IC = Indirect Costs, IR = Infrastructural Risks, OB = Operational Benefits, OC = Organisational Costs, PB = Project Benefits, PC = Project Costs, RF = Risk Factor, SB = Strategic Benefits, SM = Strategic medium-term benefit, SR = Systemic Risks, TB = Tangible Benefits, TC = Tangible Costs, TL = project lead time, TR = Technological Risks, V= Project Value

Multiobjective strategies for New Product Development in the pharmaceutical industry

New Product Development (NPD) constitutes a challenging problem in the pharmaceutical industry, due to the characteristics of the development pipeline. Formally, the NPD problem can be stated as follows: select a set of R&D projects from a pool of candidate projects in order to satisfy several criteria (economic profitability, time to market) while coping with the uncertain nature of the projects. More precisely, the recurrent key issues are to determine the projects to develop once target molecules have been identified, their order and the level of resources to assign. In this context, the proposed approach combines discrete event stochastic simulation (Monte Carlo approach) with multiobjective genetic algorithms (NSGAII type, Non-Sorted Genetic Algorithm II) to optimize the highly combinatorial portfolio management problem. In that context, Genetic Algorithms (GAs) are particularly attractive for treating this kind of problem, due to their ability to directly lead to the so-called Pareto front and to account for the combinatorial aspect. This work is illustrated with a study case involving nine interdependent new product candidates targeting three diseases. An analysis is performed for this test bench on the different pairs of criteria both for the bi- and tricriteria optimization: large portfolios cause resource queues and delays time to launch and are eliminated by the bi- and tricriteria optimization strategy. The optimization strategy is thus interesting to detect the sequence candidates. Time is an important criterion to consider simultaneously with NPV and risk criteria. The order in which drugs are released in the pipeline is of great importance as with scheduling problems

Does vertical integration reduce investment reluctance in production chains? An agent-based real options approach

This paper uses an agent-based real options approach to analyze whether stronger vertical integration reduces investment reluctance in pork production. A competitive model in which firms identify optimal investment strategies by using genetic algorithms is developed. Two production systems are compared: a perfectly integrated system and a system in which firms produce either the intermediate product (piglets) or the final product (pork). Simulations show that the spot market solution and the perfectly integrated system lead to a very similar production dynamics even with limited information on production capacities. The results suggest that, from a pure real options perspective, spot markets are not significantly inferior to perfectly integrated supply chains.real options, supply chain, agent-based models, genetic algorithms, Agribusiness, Agricultural and Food Policy, Agricultural Finance, Institutional and Behavioral Economics, Productivity Analysis,

Non Expectations and Adaptive Behaviours: the Missing Trade-off in Models of Innovation

We explore the modelling of the determination of the level of R&D investment of firms. This means that we do not tackle the decision of being an innovator or not, nor the adoption of a new technology. We exclude these decisions and focus on the situations where firms invest in internal R&D in order to produce an innovation. In that case the problem is to determine the level of R&D investment. Our interest is to analyse how expectation and adaptation can be combined in the modelling of R&D investment rules. In the literature both dimensions are generally split up: rational expectations are assumed in neoclassical models whereas alternative approaches (institutional and/or evolutionary) generally adopt a purely adaptive representation.Bounded rationality, learning, expectations, innovation dynamics.