Testing Prospect Theory in a Deterministic Multiple Criteria Decision-Making Environment*

Prospect theory by Kahneman and Tversky [7] is tested in a deterministic multiple criteria decision-making context. In two experiments conducted in classroom settings subjects made pairwise preference comparisons of condominiums for sale. The results of the experiments indicate that the traditional value model did not explain the subjects' revealed preferences as well as the prospect model. We conclude that prospect theory is a reasonable model of choice for many individuals in such a context.     

On the influence of a biased decision support model on subjects’ behavior

Summary  The role of decision tools in managerial decisions can be crucial. Yet, there is very little research seeking to understand and measure the influence of planning models on decision-makers’ actual behavior or performance. We focus on investigating, in an experimental context, the influence of a decision model (time allocation model) on student subjects’ changes of mind and behavior. Of special interest is the effect of the articulated importance of criteria on subjects’ choices. Furthermore, we have also studied how the above mentioned model influences subjects’ actual (measurable) performance. The results are partly conflicting, though interesting: (1) the (biased) decision model did have a significant influence on the subjects’ choices, (2) when the model provided additional freedom to improve criterion values, the subjects’ choices were rather inconsistent with the articulated importance of criteria, (3) the interpretation is not clear, how plans influence actual behavior, and (4) the subjects did not fully recognize the bias of the model (when present). However, our findings very clearly demonstrate that model developers have a great responsibility. Biased models may guide managers to make dramatically erroneous decisions in bona fide. We hope that our findings generate additional research into this important, but under-researched topic.      

PSS business model conceptualization and application*

The discussion about business models has gained considerable attention in the last decade. Business model frameworks have been developed in the literature as management methods helping companies to comprehend and analyse their current business logic and guide the deployment of new strategies. In response to calls for a deeper understanding of the application of a business model approach to product-service systems (PSS), this study develops a two-level hierarchical framework that (i) includes a set of components with pertinent, second-order variables to take into account when undergoing the shift from products to solutions; (ii) supports industrial companies, especially SMEs, in designing their future business model and in consistently planning the actions needed to implement it. The framework was applied and refined within real-life settings. The application to KINE – a robot solutions supplier – shows how key challenges faced by servitization firms may be thoroughly addressed through the adoption of a business model perspective.     

Impact of Number of Interactions,Different Interaction Patterns,and Human Inconsistencies on Some Hybrid Evolutionary Multiobjective Optimization Algorithms*

We investigate the impact of the number of human–computer interactions, different interaction patterns, and human inconsistencies in decision maker responses on the convergence of an interactive, evolutionary multiobjective algorithm recently developed by the authors. In our context “an interaction” means choosing the best and worst solutions among a sample of six solutions. By interaction patterns we refer to whether preference questioning is more front‐, center‐, rear‐, or edge‐loaded. As test problems we use two‐ to four‐objective knapsack problems, multicriteria scheduling problems, and multiobjective facility location problems. In the tests, two different preference functions are used to represent actual decision maker preferences, linear and Chebyshev. The results indicate that it is possible to obtain solutions that are very good or even nearly optimal with a reasonable number of interactions. The results also indicate that the algorithm is robust to minor inconsistencies in decision maker responses. There is also surprising robustness toward different patterns of interaction with the decision maker. The results are of interest to the evolutionary multiobjective (EMO) community actively developing hybrid interactive EMO approaches.     

Estimation and prediction for low degree polynomial models under measurement errors with an application to forest harvesters

Summary.  In a modern computer-based forest harvester, tree stems are run in sequence through the measuring equipment root end first, and simultaneously the length and diameter are stored in a computer. These measurements may be utilized for example in the determination of the optimal cutting points of the stems. However, a problem that is often passed over is that these variables are usually measured with error. We consider estimation and prediction of stem curves when the length and diameter measurements are subject to errors. It is shown that only in the simplest case of a first-order model can the estimation be carried out unbiasedly by using standard least squares procedures. However, both the first- and the second-degree models are unbiased in prediction. Also a study on real stem is used to illustrate the models that are discussed.     

Sustainability Evaluation Frameworks and Alternative Analytical Scenarios of National Economies

In this article a macro-oriented conceptual framework of the conditions necessary for the advancement of sustainability within an economy and society is formulated and analysed. The theoretical framework presented is a set of logical identities, which define relationships between the total environmental stress (TES) and the basic indicators of economic, technological and social development. The framework, called the Total Environmental Stress Approach, provides necessary but not sufficient conditions for advancing sustainability. Scenario alternatives utilizing the TES approach with data from Finland are presented to evaluate the theoretical framework.     

On the geometry of multivariate L 1 objective functions

Summary: Two multivariate L ₁ objective functions, namely the k–variate extensions of the classical mean deviation and mean difference, are considered. The duality between the original data vectors and the hyperplanes going through the origin and k – 1 data points is discussed and, consequently, different interesting representations and interpretations of the multivariate mean deviation are introduced. A similar duality is found between the lift data vectors and the hyperplanes going through k data points leading to different representations of the multivariate mean difference. The objective functions are also shown to have interpretations in terms of the centers of facets of the data based zonotopes and lift zonotopes. Moreover, interchanging the roles of the data vectors and the data hyperplanes yields nonparametric measures of (angular) distances between the data vectors as well as between the hyperplanes. Finally, multivariate sign and rank based tests and estimates in the one–sample and several–samples multivariate cases are discussed to illustrate the theory.*The authors wish to thank the referees for valuable comments and suggestions. The research was partially supported by the Academy of Finland.     

Preference-Order Recursion for Finding Relevant Pure,Admissible and Optimal Statistical Decision Functions*

A preference-order recursion algorithm for obtaining a relevant subset of pure, admissible (non-dominated, efficient) decision functions which converges towards an optimal solution in statistical decision problems is proposed. The procedure permits a decision maker to interactively express strong binary preferences for partial decision functions at each stage of the recursion, from which an imprecise probability and/or utility function is imputed and used as one of several pruning mechanisms to obtain a reduced relevant subset of admissible decision functions or to converge on an optimal one. The computational and measurement burden is thereby mitigated significantly, for example, by not requiring explicit or full probability and utility information from the decision maker. The algorithm is applicable to both linear and nonlinear utility functions. The results of behavioral and computational experimentation show that the approach is viable, efficient, and robust.