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Abstract. R&D portfolio management is a critical task with which the majority of the large companies are confronted. Despite its wide implementation in companies there are no widely accepted and used methods to perform this task. Each company uses its own mix of various qualitative and quantitative methods to achieve its goal. The objective of this thesis is to explore the adequacy and the design issues to use a prediction market for supporting the R&D portfolio management process. We chose prediction markets to perform this task since their aggregation mechanisms and information discovery process seems to solve most of the current issues of the R&D portfolio management process.

R&D portfolio management is a critical task with which the majority of the large companies are confronted. Despite its wide implementation in companies, there are no widely accepted and used methods to perform this task. Each company uses its own mix of various qualitative and quantitative methods to achieve its goal. The objective of this thesis is to explore the adequacy to use a prediction market for supporting the R&D portfolio management process. We chose prediction markets to perform this task since their aggregation mechanisms and information discovery process seems to solve most of the current issues of the R&D portfolio management process. PREDICTION MARKETS AS AN INNOVATIVE WAY TO MANAGE R&D PORTFOLIOS 1

The main objective of this research is to use prediction markets as negotiation agents, for supporting R&D portfolio management. To support this research, we iteratively designed, developed, operated and evaluated several prototypes. We start by presenting the weaknesses of the current techniques for managing R&D portfolio. Then, we intend to demonstrate that prediction markets correct these weaknesses in R&D portfolio management. Furthermore, following a design science paradigm, we illustrate the design of our artifacts using build-andevaluate loops supported with a field study, which consisted in operating the prediction markets in different settings. 1.

Selection of a project portfolio with limited resources is an important decision in both public and industrial organizations. Often these decisions are complicated by a high number of project proposals, considerations of multiple criteria and resources, project interactions, uncertain project outcomes and incomplete information about decision maker's (DM's) preferences. Moreover, opportunity costs of funding projects often make choice of the total resource expenditure an integral part of project portfolio selection. This thesis presents a decision analytic framework, entitled Robust Portfolio Modeling (RPM), whose novelty lies in i) the modeling of incomplete information about the DM's preferences and projects ' properties and ii) identifying robust project and portfolio decisions in view of such incomplete information subject to varying levels of resource expenditure. Under such incomplete information, the concept of a single optimal portfolio is generalized to the set of non-dominated portfolios. Based on the computation of nondominated portfolios, robust core projects included in all non-dominated portfolios can be identi ed. Similarly, exterior projects are not included in any non-dominated portfolios

In this paper we study how the selection of partners in a virtual organisation (VO) can be assisted through mixed integer linear programming (MILP) models. Additionally to fixed and variable costs, we present extensions that accommodate transportation costs, capacity risk-measures, and inter-organisational dependencies such as the success of past collaboration. Experiences from a real case study indicate that these models are helpful in VO decision making; computational experiments suggest that the models are tractable. In general, the MILP models are potentially applicable to a variety of portfolio selection problems.

Abstract This paper provides an overview of the one-stage R&D portfolio optimization problem. It provides a novel problem model that can be solved with stochastic combinatorial optimization methods. Current solution methods are reviewed and a new method that scales to large problems, Stochastic Gradient Portfolio Optimization (SGPO), is proposed. Although SGPO is a heuristic method, we prove global convergence in certain conditions. SGPO is numerically compared to current optimization methods on a test case involving Solid Oxide Fuel Cells. Keywords R&D portfolio · Stochastic combinatorial optimization · Solid oxide fuel cell 1

Evaluating the economic impact of cognitive radio