Research

Abstract: Complexity, resulting from interactions among many components, is a characterizing feature of healthcare systems and related decisions. It scales up in the face of pandemics that give rise to multiple sources of uncertainty and where various contextual factors interact with each other and with policy parameters that combine to yield outcome distributions. This paper proposes a unified agent-based modeling framework to derive qualitative insights that assist and inform policy decisions related to pandemics. The general framework comprises a contagion model that explicates exogenous policy-relevant variables, as well as their links with features of the environment in which the policy decisions will be implemented. Furthermore, the framework identifies sources of uncertainty at different system layers. The characterization of the macro level, for example, as manifested in the network structure, encompasses two constitutive factors. These two factors, in turn, capture much of the stochasticity that results from the network’s inherent randomness. By synthesizing the model components further into a broader agent-based model, the current framework also accounts for heterogeneous micro-level attributes that collectively yield macrolevel outcomes. Several stylized examples help establish insights into the overall tendency of complex systems to produce multidimensional outputs. A comprehensive, controlled, computational experiment offers further evidence across a range of scenarios and various policy conditions. 

Abstract: Companies rely widely on coopetition, that is, cooperation with competitors, to foster their innovation. However, despite its increasing popularity, the impact of coopetition on innovation remains unclear. Different theories predict different results, and empirical evidence suggests that coopetition can either improve or reduce innovation performance, or even make no impact. The lack of consensus makes further exploration of this important relationship essential. Our study attempts to shed new light on the impact of sourcing from competitors on innovation, using a data-driven exploratory model that needs no prior specification of the direction and form of this relationship. We use a recently developed analytical method based on the preference disaggregation approach to analyze data from a sample of 112 firms operating in the petroleum, chemical, and pharmaceutical industries. We find that sourcing from competitors fosters the innovation performance of firms with financial constraints (small firms receiving no financial support from the government for innovation). In contrast, our results show that sourcing from competitors has a non-linear negative impact on large and small firms receiving such financial support, and that sourcing from competitors is much riskier for small firms in this category. Our findings contribute to the coopetition for innovation literature by highlighting that the relationship between sourcing from competitors and innovation performance depends on firm characteristics, i.e., size and financial capability. Our use of a preference disaggregation method confirms its value when studying relationships between variables where the conceptual and empirical evidence leaves this relationship unclear.  

Abstract: A common approach in decision analysis and choice modeling is to infer a preference model in the form of a value function from the holistic choice examples. This paper introduces an analytical framework for estimating individuals’ preferences through uncovering structural patterns that regulate general shapes of value functions. We suggest a simple characterization of structural patterns and investigate the impact of incorporating information on such patterns on the predictive validity and estimation accuracy of preferences through an exhaustive simulation study and analysis of real decision makers’ preferences. We found that accounting for the structural patterns at the population level considerably improves the predictive performance of the constructed value functions at the individual level. This finding is confirmed across a wide range of settings with different levels of heterogeneity among the individuals and various complexity levels in their true preferences. We found, however, that improvement in the predictive performance is more significant when the choice examples come from a larger number of individuals, and when a smaller amount of preference information is available. The proposed model is developed based on a convex optimization problem with linear constraints, thus being computationally efficient and applicable to datasets of realistic size.  

Abstract: The conventional preference disaggregation approaches for multiple criteria sorting aim at reconstructing an entire set of assignment examples provided by a Decision Maker (DM) with a single preference model instance. In case the DM’s holistic preference information is not consistent with an assumed model, one needs to accept that some assignment examples are not reproduced. We propose a new approach for handling inconsistency in the context of a threshold-based value-driven sorting procedure. Specifically, we introduce preference disaggregation methods for reconstructing all assignment examples with a set of complementary preference models. The proposed approach builds on the assumption that the importance of particular criteria or, more generally, the shape of marginal value functions and their maximal shares in the comprehensive value are contingent (i.e., dependent) on the performance profile of a given alternative. Therefore, in case of inconsistency, the set of assignment examples is divided into subsets, each of which is reconstructed by a unique model to be used only if certain circumstances are valid. We present three methods for learning a set of contingent models, allowing different degrees of variation in the contingent models along two dimensions: the shape of marginal value functions and interrelations between the models. To apply such a set for classification of non-reference alternatives, we learn a decision tree which makes the application of a given model dependent on the alternatives’ profiles represented by the performances on particular criteria, hence allowing to select an appropriate model among the competing models to evaluate a non-reference alternative. The method’s applicability is demonstrated on a problem of evaluating research units representing different fields of science. 

Abstract: An additive value function is one of the prevailing preference models in Multiple Criteria Decision Aiding (MCDA). Its indirect elicitation through pairwise questions is often applied due to lowering the cognitive effort on the part of a Decision Maker (DM). A practical usefulness of this approach is influenced by both expressiveness of the assumed model and robustness of the recommendation computed with its use. We experimentally evaluate the above characteristics in view of using an additive value function in the preference disaggregation context. The simulation results are quantified with the following four measures: (1) the share of decision scenarios for which a set of compatible value functions is non-empty, (2) the minimal difference between comprehensive values of reference alternatives compared pairwise by the DM, (3) the number of pairs of alternatives for which the necessary preference relation confirmed by all compatible functions holds, and (4) the number of non-trivial certain inferences which cannot be derived directly from the preference information. We discuss how these measures are influenced by the settings with different numbers of alternatives, criteria, pairwise comparisons, and performance distributions. We also study how the results change when applying various procedures for selection of the characteristic points which define the shape of per-criterion marginal value functions. In this regard, we compare four existing discretization algorithms with a new supervised technique proposed in this paper. Overall, we indicate that expressiveness and robustness are contradictory objectives, and a compromise between them needs to be reached to increase the usefulness of an additive value model in the preference disaggregation methods. 

Abstract: A new framework for preference disaggregation in multiple criteria decision aiding is introduced. The proposed approach aims to infer non-monotonic additive preference models from a set of indirect pairwise comparisons. The preference model is presented as a set of marginal value functions and the discriminatory power of the inferred preference model is maximized against its complexity. To infer a value function that is compatible with the supplied preference information, the proposed methodology leads to a linear programming optimization problem that is easy to solve. The applicability and effectiveness of the new methodology is demonstrated in a thorough experimental analysis covering a broad range of decision problems.