Eawag - Swiss Federal Institute of Aquatic Science and Technology

Um Umweltentscheidungen zu unterstützen, z. B. Entscheidungen im Wassersektor, braucht es strukturierte Methoden. Die Multikriterielle Entscheidungsanalyse – oder MCDA – ist eine solche Methode, die zum Ziel hat, einen Konsens zwischen Akteuren mit unterschiedlichen Meinungen zu finden. Die Akteure werden traditionellerweise mit Interviews über ihre Präferenzen befragt. Diese Präferenzen betreffen die Auswirkungen einer Reihe von Entscheidungsoptionen. Spezifisch werden Akteure z. B gebeten, die Erreichung verschiedener Ziele zu bewerten, also den Zielen die in dieser Entscheidung eine Rolle spielen, Gewichte zu geben. Das ist eine schwierige Aufgabe, weil die Konsequenzen solcher Gewichte schwierig zu verstehen sind, weil Menschen sehr schnell und systematisch zu Voreingenommenheiten während der Erhebung von Präferenzen neigen und auch weil die Interviewerin den Interviewten beeinflussen kann, wenn sie ihn bei solchen Prozessen unterstützt.

Diese Projekt hat zum Ziel, Lernspiele (sogenannte ‘Serious games’) während der Phase der Präferenzerhebung im Entscheidungsprozess einzuführen. Lernspiele sind Spiele, welche nicht primär der Unterhaltung dienen.

Die Studie besteht aus zwei Teilen:

Können Lernspiele als Element des MCDA-Prozesses die Erhebung von Präferenzen unterstützen und kann die Visualisierung der Resultate als Teil des Spieles auch als Konsistenz-Check genutzt werden?
Können Lernspiele in der MCDA den Einbezug eines breiteren Publikums bezüglich Anzahl Personen und Diversität ermöglichen? Wäre das hilfreich, um zu einer Konsens-Entscheidung zu gelangen?

Eine Literaturrecherche über den Einsatz von Lernspielen im Umweltmanagement wird es erlauben, Charakteristiken zu identifizieren, die für unseren Zweck wichtig sind: die Formulierung von Präferenzen, die Bewertungsprozesse in Entscheidungen und den Einbezug eines breiteren Publikums.

Nach dieser Recherche wird ein Spiel – oder ein Set von Spielen – in angewandten Fallstudien getestet um die Charakteristika des Spiels bezüglich unserer Ziele zu analysieren. Dabei werden wir die Resultate verschiedener Ansätze vergleichen (mit Spiele VS ohne Spiele). Das Verhalten der Spielerinnen und Spieler wird auch untersucht um herauszufinden, ob wir eine Typologie von Präferenzen finden können. Die Vor- und Nachteile des Einsatzes von Lernspielen in der MCDA sollen klar formuliert werden um die zukünftige Entwicklung von MCDA zu unterstützen.

PD Dr. Judit Lienert Gruppenleiterin, Gruppe: DA Tel. +41 58 765 5574 Inviare e-mail

Dr. Alice Aubert

ZHAW Life Sciences und Facility Management
Institut für Umwelt und Natürliche Ressourcen

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      authors => protected'Haag,&nbsp;F.; Aubert,&nbsp;A.&nbsp;H.; Lienert,&nbsp;J.' (56 chars)
      title => protected'ValueDecisions, a web app to support decisions with conflicting objectives, 
         multiple stakeholders, and uncertainty' (114 chars)
      journal => protected'Environmental Modelling and Software' (36 chars)
      year => protected2022 (integer)
      volume => protected150 (integer)
      issue => protected'' (0 chars)
      startpage => protected'105361 (19 pp.)' (15 chars)
      otherpage => protected'' (0 chars)
      categories => protected'multi criteria decision analysis; MCDA software; multi-attribute value theor
         y; environmental decision analysis; open source; population survey' (142 chars)
      description => protected'Complex environmental and public policy decisions profit from structured pro
         cedures such as multi-criteria decision analysis (MCDA). To support such dec
         isions, the new open source application ValueDecisions provides advanced ana
         lysis and visualization with no programming expected from users. Based on mu
         lti-attribute value theory (MAVT), it offers analysis for decisions with con
         flicting and interacting objectives, multiple stakeholders, and uncertain co
         nsequences of options. Programmed in R, the shiny web framework makes it acc
         essible via a graphical user interface in the browser. We exemplify using Va
         lueDecisions for a wastewater infrastructure planning case in the Paris regi
         on. We surveyed preferences of 655 citizens and conducted sensitivity analys
         is of preference parameters. The best management options were robust across 
         a range of preference profiles and assumptions. To evaluate the app, we deve
         loped a novel usability test based on the ISO standard for software quality 
         and surveyed students using ValueDecisions for case studies.' (1048 chars)
      serialnumber => protected'1364-8152' (9 chars)
      doi => protected'10.1016/j.envsoft.2022.105361' (29 chars)
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      authors => protected'Aubert,&nbsp;A.&nbsp;H.; Esculier,&nbsp;F.; Lienert,&nbsp;J.' (60 chars)
      title => protected'Recommendations for online elicitation of swing weights from citizens in env
         ironmental decision-making' (102 chars)
      journal => protected'Operations Research Perspectives' (32 chars)
      year => protected2020 (integer)
      volume => protected7 (integer)
      issue => protected'' (0 chars)
      startpage => protected'100156 (13 pp.)' (15 chars)
      otherpage => protected'' (0 chars)
      categories => protected'Behavioral Operational Research; OR in environment and climate change; learn
         ing; Multi-Criteria Decision Analysis; Decision Support System; public parti
         cipation; e-democracy' (173 chars)
      description => protected'There is a growing demand for public participation in environmental decision
         -making. However, it is unclear how a large number of citizens can best enga
         ge in such complex public policy decision processes. This need from the civi
         l society challenges the OR community to develop online decision-making tool
         s. This article reports on a feasibility assessment of swing weight elicitat
         ion, implemented online, for real-world decisions about future wastewater in
         frastructure. Eliciting weights with the swing method is common in MAVT/MAUT
         , but not online. A total of 298 affected citizens from the Paris region ans
         wered the online swing weight elicitation survey. Another 357 citizens direc
         tly rated objectives. Three aspects of learning in the context of MCDA were 
         considered: did participants learn facts about the wastewater topic? Did the
         y comply with the swing elicitation process, i.e. follow the instructions? D
         id participants learn about their preferences? Factual learning was limited.
          Process compliance was really low (12%), leading to a number of recommendat
         ions for improving the interface for online swing weight elicitation. The co
         llected preferences differed statistically significantly between the complia
         nt and non-compliant participants, and also between the non-compliant and di
         rect rating respondents. This emphasised the effect of the elicitation metho
         d on preference construction. Moreover, more participants experienced a stre
         ngthening of pre-existing opinions than a change in opinion, and most report
         ed being uncertain about their answers. This calls for better understanding 
         process learning and preference construction. We discuss our developed proce
         dure for online swing weight elicitation, recommend ways to improve swing on
         line surveys, and suggest interesting future research lines that would allow
          empirically verifying our propositions.' (1864 chars)
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      authors => protected'Aubert,&nbsp;A.&nbsp;H.; Lienert,&nbsp;J.' (41 chars)
      title => protected'Gamified online survey to elicit citizens' preferences and enhance learning 
         for environmental decisions' (103 chars)
      journal => protected'Environmental Modelling and Software' (36 chars)
      year => protected2019 (integer)
      volume => protected111 (integer)
      issue => protected'' (0 chars)
      startpage => protected'1' (1 chars)
      otherpage => protected'12' (2 chars)
      categories => protected'' (0 chars)
      description => protected'Multi-Criteria Decision Analysis (MCDA) requires a critical step, namely to 
         elicit individual preferences. On the basis of learning theories, we formali
         ze preference construction as learning about facts and values, and as a proc
         ess; we also conceptualize an online preference elicitation survey that offe
         rs learning loops to increase factual learning and support preference constr
         uction. Another originality is gamification. Game elements (a narrative and 
         non-player characters as motivational affordance) keep respondents engaged i
         n the demanding task of weight elicitation. Our tool enables broad public pa
         rticipation in MCDA, allowing reliable online preference elicitation. The su
         rvey concept was tested with 107 students and a control treatment. Quantitat
         ive and qualitative data indicate that the concept works. Participants’ fa
         ctual knowledge increased. The survey helped students to learn about their o
         wn preferences concerning the importance of objectives. The practical implic
         ation is that weighting can be reliably elicited by online surveys. Particip
         ants reported a positive experience; further ways to improve it are thorough
         ly discussed.' (1153 chars)
      serialnumber => protected'1364-8152' (9 chars)
      doi => protected'10.1016/j.envsoft.2018.09.013' (29 chars)
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      authors => protected'Aubert,&nbsp;A.&nbsp;H.; Bauer,&nbsp;R.; Lienert,&nbsp;J.' (57 chars)
      title => protected'A review of water-related serious games to specify use in environmental Mult
         i-Criteria Decision Analysis' (104 chars)
      journal => protected'Environmental Modelling and Software' (36 chars)
      year => protected2018 (integer)
      volume => protected105 (integer)
      issue => protected'' (0 chars)
      startpage => protected'64' (2 chars)
      otherpage => protected'78' (2 chars)
      categories => protected'Multi-Criteria Decision Analysis; sustainability; serious game; gamification
         ; stakeholder participation; behavioral operational research' (136 chars)
      description => protected'Serious games and gamification are nowadays pervasive. They are used to comm
         unicate about science and sometimes to involve citizens in science (e.g. cit
         izen science). Concurrently, environmental decision analysis is challenged b
         y the high cognitive load of the decision-making process and the possible bi
         ases threatening the rationality assumptions. Difficult decision-making proc
         esses can result in incomplete preference construction, and are generally li
         mited to few participants. We reviewed 43 serious games and gamified applica
         tions related to water. We covered the broad diversity of serious games, whi
         ch could be explained by the still unsettled terminology in the research are
         a of gamification and serious gaming. We discuss how existing games could be
         nefit early steps of Multi-Criteria Decision Analysis (MCDA), including prob
         lem structuring, stakeholder analysis, defining objectives, and exploring al
         ternatives. We argue that no existing game allows for preference elicitation
         ; one of the most challenging steps of MCDA. We propose many research opport
         unities for behavioral operational research.' (1108 chars)
      serialnumber => protected'1364-8152' (9 chars)
      doi => protected'10.1016/j.envsoft.2018.03.023' (29 chars)
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Haag, F.; Aubert, A. H.; Lienert, J. (2022) ValueDecisions, a web app to support decisions with conflicting objectives, multiple stakeholders, and uncertainty, Environmental Modelling and Software, 150, 105361 (19 pp.), doi:10.1016/j.envsoft.2022.105361, Institutional Repository

Aubert, A. H.; Esculier, F.; Lienert, J. (2020) Recommendations for online elicitation of swing weights from citizens in environmental decision-making, Operations Research Perspectives, 7, 100156 (13 pp.), doi:10.1016/j.orp.2020.100156, Institutional Repository

Aubert, A. H.; Lienert, J. (2019) Gamified online survey to elicit citizens' preferences and enhance learning for environmental decisions, Environmental Modelling and Software, 111, 1-12, doi:10.1016/j.envsoft.2018.09.013, Institutional Repository

Aubert, A. H.; Bauer, R.; Lienert, J. (2018) A review of water-related serious games to specify use in environmental Multi-Criteria Decision Analysis, Environmental Modelling and Software, 105, 64-78, doi:10.1016/j.envsoft.2018.03.023, Institutional Repository

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Abteilung Umweltsozialwissenschaften

Lernspiele (Serious Games) als Element von Multikriterieller Entscheidungsanalyse

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