EURO Journal on Decision Processes最新文献

E-banking offers clients unparalleled convenience but also exposes them to potential fraud from cyber criminals. Traditionally, banks use technical security measures to ameliorate these kinds of threats. These measures, while essential, are not universally efficacious in preventing fraud. It would be wise to augment technical measures with softer measures such as behavioural interventions (i.e., nudges). In this paper, we report on the effectiveness of behavioural nudges designed to dissuade opportunistic “others” from committing e-banking fraud. Here, we report on an investigation into the impact of the deployment of a number of behavioural nudges in an e-banking customer interface. We evaluated their impact through semi-structured interviews with e-banking customers in the United States of America. We found that nudges which emphasise empathy and heightened awareness of traditional security measures were remarkably effective in dissuading dishonesty. Notably, deployment immediately after login yielded optimal results. Our findings highlight the potential of behavioural nudges to reduce e-banking fraud, thereby augmenting traditional technical countermeasures. We conclude with recommendations for future research.

We consider multiobjective combinatorial optimization problems handled by preference-driven efficient heuristics. They look for the most preferred part of the Pareto front based on some preferences expressed by the user during the process. In general, the Pareto set of efficient solutions is searched for in this case. However, obtaining the Pareto set does not solve the decision problem since one or more solutions, being the most preferred for the user, have to be selected. Therefore, it is necessary to elicit their preferences. What we are proposing can be seen as one of the first structured methodologies in facility location problems to search for optimal solutions taking into account the preferences of the user. To this aim, we use an interactive evolutionary multiobjective optimization procedure called NEMO-II-Ch. It is applied to a real-world multiobjective location problem with many users and many facilities to be located. Several simulations have been performed. The results obtained by NEMO-II-Ch are compared with those obtained by three algorithms knowing the user’s “true” value function that is, instead, unknown to NEMO-II-Ch. They show that in many cases, NEMO-II-Ch finds the best subset of locations more quickly than the methods knowing the whole user’s true preferences.

Waste sorting is an important recovery process in circular product systems. Engineering sciences deal with its performance measurement by analysing various parameter settings based on technical key figures derived from object and component quantities. They are difficult to aggregate, especially since there are no prices for all inputs and outputs. Data Envelopment Analysis (DEA) allows an aggregation without implementing prices or weight restrictions. This paper shows how DEA can be used as performance measurement method for sorting processes, whose purpose it is to separate different material components from a waste mixture as effectively and efficiently as possible. This is achieved when the individual components are sorted out into their respective recycling bins with as little effort as possible and without allowing too many incorrect throws. In this respect, sorting processes represent pure material reallocations – and no material transformations. Thus, valid material balances of sorting technologies do not allow for free disposability. A new multiple criteria DEA approach is suggested, which uses input and output objects that entirely consist of components determining the object's value. Different from common DEA approaches, the technological perspective of modelling the underlying production system is strictly separated from the preference perspective of modelling multiple performance criteria for the relevant input and output types and sorts. In more detail, we develop DEA models for a combined evaluation of operating and quality efficiency where both inputs and outputs are characterised by sorts or grades of varying quality. In contrast to existing approaches that map quality into DEA we reflect upon the underlying technological assumptions and analyse which dominance relations result from that in our case of waste sorting.

The generation of alternative policies is essential in complex decision tasks with multiple interests and stakeholders. A diverse set of policies is typically desirable to cover the range of options and objectives. Decision modelling literature has often assumed that clearly defined decision alternatives are readily available. This is not a realistic assumption in practice. We present a structured process model for the generation of policy alternatives in settings that include non-quantifiable elements and where portfolio optimisation approaches are not applicable. Behavioural issues and path dependence as well as heuristics and biases which can occur during the process are discussed. The behavioural experiment compares policy alternatives obtained by using two different portfolio generation techniques. The results of the experiment demonstrate that path dependence can occur in policy generation. We report thinking patterns of subjects which relate to biases and heuristics.

This study introduces a novel approach to effectively and efficiently solve Multi-Attribute Decision Making (MADM) problems with a considerable number of attributes. We demonstrate the need to categorize the attributes and facilitate a more systematic expert comparison. Our proposed method utilizes pairwise comparisons to assess attributes without requiring additional computations to evaluate the level of consistency. The proposed method offers greater flexibility and precision with reduced computational complexity. We present a comparative analysis with a widely used numerical example in the MADM literature to demonstrate the effectiveness and efficacy of the method proposed in this study.