Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making

In multi-objective decision planning and learning, much attention is paid to producing optimal solution sets that contain an optimal policy for every possible user preference profile. We argue that the step that follows, i.e, determining which policy to execute by maximising the user's intrinsic utility function over this (possibly infinite) set, is under-studied. This paper aims to fill this gap. We build on previous work on Gaussian processes and pairwise comparisons for preference modelling, extend it to the multi-objective decision support scenario, and propose new ordered preference elicitation strategies based on ranking and clustering. Our main contribution is an in-depth evaluation of these strategies using computer and human-based experiments. We show that our proposed elicitation strategies outperform the currently used pairwise methods, and found that users prefer ranking most. Our experiments further show that utilising monotonicity information in GPs by using a linear prior mean at the start and virtual comparisons to the nadir and ideal points, increases performance. We demonstrate our decision support framework in a real-world study on traffic regulation, conducted with the city of Amsterdam.Comment: AAMAS 2018, Source code at https://github.com/lmzintgraf/gp_pref_elici

Decision for reconstructive interventions of the upper limb in individuals with tetraplegia: the effect of treatment characteristics

Objective: To determine the effect of treatment characteristics on the\ud decision for reconstructive interventions for the upper extremities (UE) in\ud subjects with tetraplegia. - \ud Setting: Seven specialized spinal cord injury centres in the Netherlands. - \ud Method: Treatment characteristics for UE reconstructive interventions were\ud determined. Conjoint analysis (CA) was used to determine the contribution\ud and the relative importance of the treatment characteristics on the decision\ud for therapy. Therefore, a number of different treatment scenarios using these\ud characteristics were established. Different pairs of scenarios were presented\ud to subjects who were asked to choose the preferred scenario of each set. - \ud Results: forty nine subjects with tetraplegia with a stable C5, C6 or C7\ud lesion were selected. All treatment characteristics significantly influenced\ud the choice for treatment. Relative importance of treatment characteristics\ud were: intervention type (surgery or surgery with FES implant) 13%, number\ud of operations 15%, in patient rehabilitation period 22%, ambulant\ud rehabilitation period 9%, complication rate 15%, improvement of elbow\ud function 10%, improvement of hand function 15%. In deciding for therapy\ud 40% of the subjects focused on one characteristic. - \ud Conclusion: CA is applicable in Spinal Cord Injury medicine to study the\ud effect of health outcomes and non-health outcomes on the decision for\ud treatment. Non-health outcomes which relate to the intensity of treatment\ud are equally important or even more important than functional outcome in the\ud decision for reconstructive UE surgery in subjects with tetraplegia

Creative Thinking and Modelling for the Decision Support in Water Management

This paper reviews the state of art in knowledge and preferences elicitation techniques. The purpose of the study was to evaluate various cognitive mapping techniques in order to conclude with the identification of the optimal technique for the NetSyMod methodology. Network Analysis – Creative System Modelling (NetSyMod) methodology has been designed for the improvement of decision support systems (DSS) with respect to the environmental problems. In the paper the difference is made between experts and stakeholders knowledge and preference elicitation methods. The suggested technique is very similar to the Nominal Group Techniques (NGT) with the external representation of the analysed problem by means of the Hodgson Hexagons. The evolving methodology is undergoing tests within several EU-funded projects such as: ITAES, IISIM, NostrumDSS.Creative modelling, Cognitive mapping, Preference elicitation techniques, Decision support

Participatory Modelling and Decision Support for Natural Resources Management in Climate Change Research

The ever greater role given to public participation by laws and regulations, in particular in the field of environmental management calls for new operational methods and tools for managers and practitioners. This paper analyses the potentials and the critical limitations of current approaches in the fields of simulation modelling (SM), public participation (PP) and decision analysis (DA), for natural resources management within the context of climate change research. The potential synergies of combining SM, PP and DA into an integrated methodological framework are identified and a methodological proposal is presented, called NetSyMoD (Network Analysis – Creative System Modelling – Decision Support), which aims at facilitating the involvement of stakeholders or experts in policy - or decision-making processes (P/DMP). A generic P/DMP is formalised in NetSyMoD as a sequence of six main phases: (i) Actors analysis; (ii) Problem analysis; (iii) Creative System Modelling; (iv) DSS design; (v) Analysis of Options; and (vi) Action taking and monitoring. Several variants of the NetSyMoD approach have been adapted to different contexts such as integrated water resources management and coastal management, and, recently it has been applied in climate change research projects. Experience has shown that NetSyMoD may be a useful framework for skilled professionals, for guiding the P/DMP, and providing practical solutions to problems encountered in the different phases of the decision/policy making process, in particular when future scenarios or projections have to be considered, such as in the case of developing and selecting adaptation policies. The various applications of NetSyMoD share the same approach for problem analysis and communication within the group of selected actors, based upon the use of creative thinking techniques, the formalisation of human-environment relationships through the DPSIR framework, and the use of multi-criteria analysis through a Decision Support System (DSS) software.Modelling, Public Participation, Natural Resource Management, Policy, Decision-Making, Governance, DSS

CP-nets: A Tool for Representing and Reasoning withConditional Ceteris Paribus Preference Statements

Information about user preferences plays a key role in automated decision making. In many domains it is desirable to assess such preferences in a qualitative rather than quantitative way. In this paper, we propose a qualitative graphical representation of preferences that reflects conditional dependence and independence of preference statements under a ceteris paribus (all else being equal) interpretation. Such a representation is often compact and arguably quite natural in many circumstances. We provide a formal semantics for this model, and describe how the structure of the network can be exploited in several inference tasks, such as determining whether one outcome dominates (is preferred to) another, ordering a set outcomes according to the preference relation, and constructing the best outcome subject to available evidence

What's it worth? An examination of historical trends and future directions in environmental valuation

The present paper reviews activity in environmental valuation by examining trends in publication rates over the past three decades. It also provides an overview of the demand for environmental valuation by academic markets and by policy markets. The results of this historical analysis suggest that there is not as much use of environmental valuation in policy analysis as could be expected given the academic efforts on this topic. The paper also provides an overview of the future directions that environmental valuation research is likely to take given current research efforts.Environmental Economics and Policy,