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Doctoral Thesis

Distributed decision-making under incomplete information

Author(s): Malekovic, Ninoslav

Publication Date: 2016

Permanent Link: https://doi.org/10.3929/ethz-a-010710706

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DISS. ETH NO. 23346

DISTRIBUTED DECISION-MAKING UNDER INCOMPLETE INFORMATION

A thesis submitted to attain the degree of

DOCTOR OF SCIENCES of ETH ZURICH

(Dr. sc. ETH Zurich)

presented by

NINOSLAV MALEKOVIC

MPA, Harvard University

born on 22. 7. 1976.

citizen of Croatia

accepted on the recommendation of

Prof. Dr. Juliana Sutanto, ETH Zurich, supervisor

Prof. Dr. Stefano Brusoni, ETH Zurich, co-examiner

2016

1

This thesis is dedicated to my loving parents,

Cela and Josip

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3

Acknowledgments

First, I would like to thank Professor Juliana Sutanto for giving me the opportunity to

work toward my doctoral degree in her group. Supportive, knowledgeable, and kind, she is a

role model academician. Without her I would not have had the opportunity to be here.

My equal gratitude goes to the co-examiner of my doctoral thesis, Professor Stefano

Brusoni. His capacity for terse discernment has already left a timeless impression. His

commitment to knowledge, as revealed by his scholarship will remain inspiring.

My gratitude also goes to other academicians who showed me the way forward on my

path, each in his own authentic way, but all of them by personal magic and example. Most

memorable among them are late Ante Fulgosi the intelligence researcher, late Alija Kulenovic

the quant in cognitive science, both of the Psychology Department of University of Zagreb,

and inimitable Clifford Taubes of Taubes’ Gromov Invariant and Harvard Mathematics

Department.

This thesis would not have been possible without the support of colleagues in the MIS

group and elsewhere. I am grateful to have been able to share my work with Mihai Calin,

Patrizia de Lorenzo, Balint Dioszegi, Basil Hess, and Onur Saglam.

Last but not least, I am grateful to my family, Josip, Cela, Lucija and Hrvoje, as well

as my friends Sari Graff, Suzana Helshani, Rosanna Monteleone, and Vjeran Skurjeni. I thank

them for being there when I needed their support.

4

5

Abstract:

This cumulative thesis investigated collaborative decision-making as an instance of

distributed decision-making. Eighty-two studies were reviewed, six research themes were

identified, and five research directions were proposed to guide further advances. These

directions recommend to examine collaborative decision-making for agents’ shared

knowledge, reasoning limits, spatiotemporal configurations, and collaborative failures.

Moreover, the thesis examined two non-collaborative tendencies that characterize a

collaborative failure: Agents’ regret avoidance behaviors and basic manipulative tendency

were analyzed under incomplete information. Relating to agents’ competing interests, features

of incomplete information influence these non-collaborative tendencies: Uncertain competition

can profile agents for their capacity to decide. Moderated by uncertain product availability, the

uncertain competition can direct agents’ regret avoidance behaviors. Ignorant of competing

interests, agents refrain from manipulating collective decisions. Otherwise, information

aggregation complexity can suppress effectiveness of agents’ manipulation, and authenticate

their disclosures. Reasonable goals can still be attained, even if agents’ collaboration fails.

These findings contribute to the understanding of distributed decision-making.

Keywords: distributed and collaborative decision-making; collaborative failures;

regret avoidance; manipulative tendency; incomplete information; uncertainty; information

asymmetry; information aggregation complexity.

6

7

Zusammenfassung:

Diese kumulative Dissertation untersucht kooperatives Entscheiden als eine Instanz des

verteilten Entscheidens. Um Fortschritte zu fördern, wurden zweiundachtzig Studien

ausgewertet, sechs Forschungsthemen bestimmt und fünf Forschungsrichtungen

vorgeschlagen. Diese Richtungen haben als Ziel, gemeinsames Entscheidungsträgerwissen,

Grenzen ihres logischen Denkens, ihre Raum-Zeit-Konfiguration und scheiternde Kooperation

zu untersuchen. Darüber hinaus, betrachtet die Dissertation zwei nicht-kooperative Tendenzen,

die eine scheiternde Kooperation charakterisieren: Die Bedauernsvermeidung der

Entscheidungsträger und ihre grundlegende Manipulationstendenz wurden unter

unvollständiger Information analysiert. Unvollständige Informationserscheinungen, die sich

auf konkurrierende Interessen beziehen, beeinflussen diese nicht-kooperative Tendenzen:

Ungewisser Wettbewerb kann die Entscheidungsfähigkeit der Entscheidungsträger auswerten.

Der von ungewisser Produktverfügbarkeit moderierte Wettbewerb kann ihre

Bedauernsvermeidung leiten. Über konkurrierende Interessen nicht informierte

Entscheidungsträger sehen davon ab, kollektive Entscheidungen zu manipulieren. Sonst kann

die Komplexität der Informationsaggregation die Effektivität der

Entscheidungsträgermanipulation abschwächen und ihre Information als echt beweisen.

Entsprechende Ziele können immer noch erreicht werden, auch wenn die

Entscheidungsträgerkooperation scheitert. Diese Ergebnisse tragen zum Wissen des verteilten

Entscheidens bei.

Schlüsselwörter: verteiltes und kooperatives Entscheiden; scheiternde Kooperation;

das Bedauern; die Manipulation; unvollständige Information; Ungewissheit;

Informationsasymmetrie; die Komplexität der Informationsaggregation.

8

Table of Contents

Acknowledgments 3 Abstract

5

Zusammenfassung

7

Table of contents

8

Introduction

10

Three Complementary Studies

12

Collaborative Decision-Making

14

Avoiding Regret in Online Auctions

16

Manipulating Distributed Decision-Making

19

Conclusion

21

References

22

Appendices

29

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10

1. Introduction

Cutting across decision, computing, and economic sciences, distributed decision-

making is a rapidly developing branch of general decision theory. “Large organizations are

split up into various divisions, and complex decision problems are separated into more

tractable components. Capable of handling and communicating ever increasing amounts of

data, these decomposed units no longer need be treated as being almost isolated but may also

be coordinated more closely” ([68], p. 1).

Solving a decision problem by efficiently separating and coordinating agents’ decisions

[67], distributed decision-making can be regarded as a means of reducing organizing and

computing complexity [68]. Diverse approaches to distributed decision-making have been

developed, including agency-based approaches, voting methods, auction designs, distributed

artificial intelligence [68] etc. “Multi-agent systems, networking and principal-agent theory

point towards these modern developments” ([68], p. 1).

Dependence of distributed decision-making on agents’ understanding of a decision

problem can make distributed decision-making collaborative [59]. Collaborative decision-

making “makes explicit the aggregation of individuals’ understandings of the frame of a

decision to be made, the alternatives to be considered, the sources of value and risk, and,

finally, the reasons for a resulting collaborative choice” ([59], p. 29). Under collaboration,

agents’ shared knowledge is the organizing principle behind efficiency of agents’ decisions

[18, 32]. If a decision problem requires collaboration, then this principle makes collaborative

decision-making “significantly more valuable… than alternatives envisioned by decision-

makers” ([59], p. 1). However, according to our review of collaborative decision-making

research, collaborative failures remain less well understood.

Characterized as agents’ lack of shared knowledge about a social or strategic interaction

[35, 58], incomplete information reveals a possibility for agents’ collaboration to fail. In

conditions of incomplete information, distributed decisions may have to be made under

uncertainty or information asymmetry. Any information made available to agents may

aggregate in complex ways with information that is already available to them [11, 15, 27].

Distributed decisions may then have to be made under variable information aggregation

complexity. Single-variable or multivariable uncertainty, information asymmetry, and

information aggregation complexity are but complementary features of incomplete

information.

11

This cumulative thesis examined an agents’ collaborative failure for effects of

incomplete information. Our research questions focus on non-collaborative behaviors

pertaining to an agents’ collaborative failure: What are some such behaviors? How do such

behaviors depend on incomplete information? If collaboration fails, can agents’ decisions

benefit from different features of incomplete information? Before answering these questions,

we discuss the logic of our research efforts.

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2. Three Complementary Studies

Responding to the above questions, the thesis reviewed the research of collaborative

decision-making since the late 1980-s. Collaborative decision-making “considers alternative

understandings of a problem, competing interests, priorities and constraints” ([43], p. 1). If

distributed decision-making emerges from agents’ communications, it accounts for agents’

competing interests and other concerns. In such conditions, distributed decision-making is

more likely to be collaborative. Otherwise, enforcing a model of distributed decision-making

onto agents is more likely to lead to their collaborative failure [67].

Our review has been submitted to Theory and Decision (Please see Appendix 1). The

review also informed our choice of non-collaborative behaviors that characterize collaborative

failures: Regret avoidance and manipulative behaviors can be elicited, by respectively

enforcing auctioning and voting onto a collection of agents. These two models are

complementary: While the former exemplifies non-collective decision-making, the latter

exemplifies collective decision-making. In addition, their enforcement complements

emergence of collaborative options, characterizing collaborative decision-making. In effect,

their enforcement frames non-collaborative behaviors characterizing a collaborative failure.

Thus, auctioning and voting were chosen for further analyses.

Two empirical studies follow the reviewed research. The first study examined agents’

regret avoidance behaviors for competition uncertainty and product availability uncertainty.

Auctioning was enforced through a website accessible by mobile phones. The article has been

submitted to Information Systems Research (Please see Appendix 2). The second study

examined agents’ manipulative imputation for information asymmetry and aggregation

complexity. Voting was enforced and implemented in a computer lab. The article was

published in Decision Support Systems (Please see Appendix 3). Table 1 summarizes the three

studies, and includes the author’s contribution.

13

Table 1 Summary of the three studies

Title Author and co-authors Author’s contributions Status

Collaborative decision-making: Literature review and research directions

Author:

Ninoslav Malekovic

Co-author:

Prof. Juliana Sutanto

Conceptual positioning

Search strategy

Literature Review

Identification of research directions

Manuscript

Submitted to Theory and Decision

Avoiding regret in online auctions: The effects of uncertain competition and uncertain product availability

Author:

Ninoslav Malekovic

Co-authors:

Dr. Lazaros Goutas

Prof. Juliana Sutanto

Conceptual positioning

Data analysis

Manuscript

Submitted to Information Systems Research

Manipulative imputation in distributed decision support settings: The implications of information asymmetry and aggregation complexity

Author:

Ninoslav Malekovic

Co-authors:

Dr. Lazaros Goutas

Prof. Juliana Sutanto

Prof. Dennis Galletta

Research design

Conceptual positioning

Literature review

Data collection and analysis

Manuscript

Published in Decision Support Systems:

Malekovic, N. Sutanto, J. Goutas, L. (2016), Decision Support Systems 85, 1–11

14

3. Collaborative Decision-Making

Our review selected and examined fifty journal and thirty-two conference articles.

Technological efforts have driven the research of collaborative decision-making since the late

1980-s (e.g., [1, 16, 22, 39]). Agent-based modeling prevailed among design research and other

technological efforts (e.g., [2, 5, 39, 41, 55]).

Our review classified these articles into six research themes. The themes range from

collaborative enterprises and conflict resolution, to collaborative reasoning and knowledge

organization, to complexity and spatiotemporal issues in collaborative decision-making. Table

2 presents the identified themes.

One research theme features standardized and emergent protocols for collaborative

enterprises (e.g., [24, 25, 26, 76, 80, 81]). Another theme pertains to agents’ conflicting

tendencies and methods to resolve them (e.g., [62, 72]). The third theme deals with reasoning

that integrates knowledge into collaborative decision-making (e.g., [43, 44]). The most

valuable effects in collaborative decision-making occur between decision-making and

knowledge-based processes [32]. “Collaborative decision-making is a core organizational

activity that comprises a series of knowledge representation and processing tasks” ([32], p. 1).

Thus, the fourth theme delves into knowledge-dependence of collaborative options [18, 19,

32]. The fifth theme focuses on collaborative complexity that can be traced to the nature of

decision problems, the number of decision criteria and the multiplicity of agents (e.g., [2, 75]).

Finally, the sixth theme is about spatiotemporal effects, and often inconclusive, reverse-causal

issues to which they can give rise (e.g., [12, 42, 57, 63]).

Our proposed research directions motivate theoretical advances in collaborative

decision-making. First, emergence of a collectively owned hybrid option from agents’ shared

knowledge has to be made a central theoretical consideration in collaborative decision-making

[59]. Conditions of such emergence have to be identified. Second, as specified by

Table 2 The identified themes

1. Collaborative enterprises 2. Conflict resolution 3. Collaborative reasoning

Extant protocols support agents’ collaborative enterprises.

If agents do not resolve conflicts, opinion factions may polarize.

Argumentation integrates agents’ knowledge in collaborative

decisions.

4. Organization of knowledge 5. Collaborative complexity 6. Spatiotemporal challenges

Agents represent shared knowledge, using ontologies.

Complexity can be traced to decision problems, criteria, and

agent multiplicity.

Agents’ spatiotemporal configurations play into collaborative decisions.

15

argumentation logics, agents’ reasoning limits bound this emergence. Therefore, implications

of reasoning limits (e.g., [14, 79]) for emergent, collaborative options have to be carefully

examined. Third, agents’ spatiotemporal configurations also bound such options. Hence,

dependence of such options on spatiotemporally configured agents has to be cogently

explained. Fourth, collaborative failures have to be profiled for mixtures of agents’ non-

collaborative behaviors. Fifth, all of these considerations have to be designed into architecture

of collaborative enterprises.

Agents’ collaborative failures are less well understood than efficiency of agents’

decisions. Such failures can be gleaned from existing decision-analytic accounts. “In

collaborative decision-making, we do not strive for an optimum, a compromise, or a satisficing

solution” ([59], p. 1). In fact, these approaches fall short of collaborative decision-making, and

exemplify failed collaboration.

Existing accounts inform our analyses. Manipulation is a symptom of a collaborative

failure [36, 69]. It includes deceptive, strategic, and rigging behaviors [6, 34, 36, 52, 53, 54].

An instance of optimization, regret avoidance behaviors also characterize collaborative failures

[3, 7, 10, 38, 51, 73, 74]. Thus, manipulative and regret avoidance behaviors were chosen for

a closer scrutiny.

16

4. Avoiding Regret in Online Auctions

Our quasi-experimental study of online auctions refers to bidders as agents. Each agent

considers whether to bid more at a risk of overpaying for a product, or to bid less, at a risk of

failing to obtain it. While the former tendency leads agents to regret winning, the latter leads

agents to regret losing. Our study examined agents’ simplest regret avoidance behaviors:

Avoidance of winner regret by underbidding and avoidance of loser regret by overbidding. The

study analyzed these behaviors for uncertainty effects, as derived from signaling theory.

No regret avoidance behavior has been empirically observed in sealed-bid auctions, i.e.,

sporadic observations were attributed to flawed methods (e.g., [21, 33, 45]). Our study

complemented the earlier findings, by building on specific strategic equivalences between

sealed-bid and open-bid auctions. Second-price sealed-bid and open ascending-bid auctions

are strategically equivalent [48]. Similarly, first-price sealed-bid and open descending-bid

auctions are strategically equivalent [48]. Drawing inferences from these equivalences, our

study examined agents’ regret avoidance behaviors in open-bid auctions. Adapted from [48],

Figure 1 depicts the logic of our contribution.

FIRST-PRICE SEALED-BID

AUCTION

OPEN DESCENDING-BID AUCTION

For independent private values, due to the strategic equivalence, these auctions should lead to

the same price. However, they were empirically found to result in different prices.

Bidders’ regret avoidance behaviors can explain the empirically different prices.

SECOND-PRICE SEALED-BID

AUCTION

OPEN ASCENDING-BID AUCTION

Our study compares agents’ regret avoidance behaviors between descending and open ascending-bid auctions. The study answers whether a non-disclosure of the second highest bid indeed guards bidders against regret.

There can be no winner regret in second-price sealed bid auctions because they mechanically guard bidders against winner regret. First-price sealed-bid auctions do not have this mechanical feature. Because no regret avoidance behaviors have been observed in first-price sealed bid auctions, there must be some regret in first-price sealed bid auctions.

Figure 1 The logic of our contribution

Due to the strategic equivalence, these auctions

should lead to the same price. However, they were empirically found to result in different prices.

Bidders’ regret avoidance behaviors do not explain the empirically different prices.

17

Agents’ regret avoidance behaviors in open-bid auctions were analyzed for competition

uncertainty and product availability uncertainty. Characterizing a difference in bid disclosures

between open ascending-bid and open descending-bid auctions, competition uncertainty refers

to an agent’s difficulty in assessing competition intensity. Product availability uncertainty

refers to an agent’s difficulty in assessing an adequate product availability. Figure 2 depicts

our research model.

Figure 2 The Research Model

Several findings were confirmed. The more uncertain the competition, the less likely is

the focal agent to avoid any regret (H1). While uncertain product availability negatively

moderates the effect of uncertain competition on the focal agent’s avoidance of winner regret

(H2a), the opposite holds true for the focal agent’s avoidance of loser regret (H2b).

Agents’ regret avoidance behaviors extract information from competition and product

availability. Regret is suggested to depend on a disclosure of two highest bids: The earlier

studies suggested that a non-disclosure of these bids would weaken agents’ regret [21, 28, 29,

30, 31, 33]. By comparing open ascending-bid and open descending-bid auctions, our study

cogently explained this consideration: Not only do agents measure regret relative to disclosed

competing bids, but they also learn from disclosed competing bids how to avoid it. By

confirming this, our study concluded the analyses of regret in auctions.

Apart from auction theory, our study contributed to signaling theory. “When

information asymmetries exist, signaling theory suggests that the interacting parties send

signals to one another in order to adjust their purchasing behaviors…” ([23], p. 37). “Signals

are particularly important in online auctions where uncertainty exists in the product, seller,

and other bidders” ([23], p. 37). By pointing out that signaling can also be influenced by

availability of products [20, 40], our study contributed to the understanding of

multidimensionality of a signaling medium.

Finally, our study of regret avoidance under uncertain competition may be extended.

Agents can stifle competition, by collusively refraining from placing bids [65]. An auctioneer

Avoidance of Winner and Loser

Regret

Availability Uncertainty

Competition Uncertainty

H1(-)

H2a(-)H2b(+)

18

can encourage competition, by placing false bids [65]. Rigging behaviors can also lead to non-

clearing market prices, e.g., bubbles and crises [4]. A study of regret in conditions of rigging

can motivate further research of regret avoidance under uncertain competition.

19

5. Manipulating Distributed Decision-Making

Our experimental study examined an agents’ basic manipulative tendency in distributed

decision-making. Supported by distributed communications, agents can attempt to manipulate

collective decisions [8, 9, 52, 53, 54]. Nonetheless, effectiveness of such behaviors is unclear

[8, 9, 52]. Thus, our study analyzed both incidence and effectiveness of agents’ manipulative

imputation. Figure 3 depicts our research model.

Figure 3 The research model with main, moderating, control, and dependent variables

Agents’ manipulative imputation was analyzed for information asymmetry and

information aggregation complexity, as derived from collective choice [6, 36] and information

aggregation theories [11, 15]. Several findings were confirmed: Providing information on

competing interests increases the incidence of the focal agent’s manipulative imputation (H1a).

Given the simplest information aggregation rule, doing so further increases the focal agent’s

effectiveness (H1b). Complexity of an aggregation rule decreases the incidence and

effectiveness of the focal agent’s manipulative imputation (H2).

Our findings contributed to our theoretical framework. This framework explains

distributed decision-making “through principles of minimization of uncertainty or

maximization of predictability under information pooling over many individuals” ([11], p.

598). It has already been known that agents inaccurately disclose information [17]. In non-

Manipulative Tendency &

Effectiveness of Manipulative Imputation

Complexity of a Decision Rule

Information Asymmetry

H1(+)

H2(-)

Control Variables

Social Value Orientation Age

Familiarity Score

Moderating Effect

Main Effect

Outcomes

Gender

Setting Types Decision Scenarios

20

collective settings, agents under information asymmetry distort information by trading [78],

and information aggregation efficiency decreases this tendency [78]. In collective settings,

agents under information asymmetry distort information by manipulating collective outcomes.

However, it is information aggregation complexity that decreases this tendency, and

authenticates agents’ information disclosures.

By allowing for more complex manipulations, our analysis can be extended. By

proposing dependent options that cannot be chosen, agents may manipulate collective decisions

[34, 49, 60]. By forming coalitions and rigging agendas, agents may do so, too [13, 34, 46, 50,

64, 77]. Agents’ bounded rationality and heuristics [37, 70, 71] may also play a role in these

issues. Such effects await further scrutiny.

21

6. Conclusion

The cumulative thesis contributes to the understanding of distributed decision-making.

Incomplete information can alleviate symptoms of an agents’ collaborative failure. Uncertain

competition can profile agents for their capacity to decide. Moderated by uncertain product

availability, uncertain competition can direct an agents’ non-collaborative tendency. Ignorant

of competing interests, agents refrain from manipulating collective decisions. Otherwise,

information aggregation complexity can decrease this tendency, and authenticate agents’

information disclosures. Reasonable goals can still be attained, even if agents fail to

collaborate.

22

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29

APPENDICES

30

APPENDIX 1

COLLABORATIVE DECISION-MAKING: LITERATURE REVIEW AND

RESEARCH DIRECTIONS

Abstract:

Collaborative decision-making is a less developed branch of decision theory. Having

outlined key properties of collaborative decision-making, we selected eighty-two articles on

this topic. We classified the articles into six themes. The research themes range from

collaborative enterprises to conflict resolution, to argumentation and organization of

knowledge, to complexity and spatiotemporal issues in collaborative decision-making. Our

proposed directions motivate further advances in this line of research. First, we propose to

identify problem-solving conditions in which a collectively owned hybrid option emerges from

agents’ shared knowledge. Second, we propose to examine how agents’ reasoning limits bound

this emergence. Third, we propose to examine how spatiotemporally configured agents bound

this emergence. Fourth, we propose to profile collaborative failures for mixtures of agents’

non-collaborative behaviors. Fifth, we propose to design all of these considerations into

architecture of collaborative enterprises. Our five directions can guide collaborative decision-

making towards a cogent theory. They can also stimulate related empirical advances.

Keywords: collaborative decision-making; literature review; research directions.

1. Introduction

Ill-structured problems are common in healthcare [8, 24, 43, 54, 70, 103],

engineering [30, 38, 52, 64, 67, 79, 80, 102, 104, 109], and public policy [6, 7, 18, 51, 56, 59,

62, 69, 77, 86, 96, 100, 125]. Solutions to such problems depend on decision-makers’

understanding [1, 39, 40, 41, 49, 95]. Aggregation of decision-makers’ understanding makes

collaborative choice significantly more valuable than alternatives envisioned by any decision-

maker [89]. Nonetheless, collaborative decision-making remains a less developed branch of

decision theory [48, 84, 89].

Strengthening the understanding of collaborative decision-making, we reviewed the

available research in scholarly databases. The selected articles largely come from two

approaches: The socio-cognitive approach is intended to overcome bounds on agents’

information-processing [3, 5, 55, 61, 82, 85, 90, 130]. The design research approach proposed

ways to formalize, manage, and integrate knowledge [39, 40, 41, 44, 49, 66, 105, 132], analyze,

31

assess and support arguments [11, 12, 63], estimate polarized opinion factions [13, 14, 15], and

build consensus [21, 91]. For collaborative decision-making, a collectively owned hybrid

option has to emerge from decision-makers’ shared knowledge [89]. However, decision-

analytic accounts of this emergence have yet to be proposed.

In response, we give five research directions. First, we propose to examine conditions

in which problem solving requires a collectively owned hybrid option to emerge from agents’

shared knowledge. Second, we propose to examine collaborative decision-making for

implications of agents’ reasoning limits. Third, we propose to examine collaborative decision-

making for effects of agents’ spatiotemporal configurations. Fourth, we propose to study the

nature of agents’ collaborative failures. Fifth, we propose to embed all of these considerations

into collaborative enterprises. Our research directions are intended to motivate theoretical

advances in collaborative decision-making.

The rest of the paper is organized as follows: The next section discusses key properties

of collaborative decision-making. The subsequent two sections explain our article search

strategy and summarize the identified research themes. The final section proposes research

directions, and concludes the paper.

2. Definitions

An early definition of collaborative decision-making was socio-cognitive [85, 90, 116].

“To decide effectively, agents need the ability to represent and maintain a model of their own

mental attitudes, reason about other agents' mental attitudes, and influence other agents'

mental attitudes” ([90], p. 107). This process ranges from identification of a problem and group

generation, to social practical reasoning and negotiation [90].

The design research definition treats argumentation as a key property of collaborative

decision-making [66]. Under this definition, collaborative choice is intended to “efficiently

capture users’ rationale, stimulate knowledge elicitation and argumentation on the issues

under consideration, while constantly checking for inconsistencies among users’ preferences

and considering the whole set of the argumentation items asserted to update the discourse

status” ([66], p. 1). This definition “considers alternative understandings of a problem,

competing interests, priorities and constraints” ([63], p. 1).

The decision-analytic definition of collaborative decision-making makes “… explicit

the aggregation of individuals’ understandings of the frame of the decision to be made, the

alternatives to be considered, the sources of value and risk, and, finally, the reasons for the

32

resulting collaborative choice” ([89], p. 29). This definition makes “collaborative connection”

central to collaborative decision-making ([89], p. 40). Combining valuable elements of extant

alternatives, agents connect to an emerging, collectively owned “hybrid option” ([89], p. 34).

A rapid understanding of collaborative options depends on agents’ “combined decision

space” ([71], p. 172). While efficiently separated and coordinated decisions motivate

distributed decision-making [81, 107, 108], dependence of options on agents’ shared

knowledge makes distributed decision-making collaborative [41].

3. Literature on Collaborative Decision-Making

On December 3rd, 2015, we searched for articles with “collaborative choice” or

“collaborative decision” appearing in titles, abstracts, or keywords. We searched for such

articles in Scopus, ProQuest, Emerald, Wiley Interscience, Google Scholar, and the Social

Science Research Network. Our selection excluded articles marginally related to the above

definitions. Included were fifty journal and thirty-two conference articles since 1988. The

research objective, theory, method, and key findings for each article are listed in Appendix A.

Table 1 presents the identified themes.

Table 2 outlines the articles relative to the research themes and theories. Only thirty-

nine out of eighty-two studies employed existing theoretical frameworks (e.g., [21, 39, 40, 55,

92]). Only four studies proposed new theoretical advances [48, 84, 89, 90].

Table 1 The identified themes

1. Collaborative enterprises 2. Conflict resolution 3. Collaborative reasoning

Extant protocols support agents’ collaborative enterprises.

If agents do not resolve conflicts, opinion factions may polarize.

Argumentation integrates agents’ knowledge in collaborative

decisions.

4. Organization of knowledge 5. Collaborative complexity 6. Spatiotemporal challenges

Agents represent shared knowledge, using ontologies.

Complexity can be traced to the nature of decision problems and criteria and agent multiplicity.

Agents’ spatiotemporal configurations play into collaborative decisions.

33

Table 2 The theme-theory classification1

THEORIES

Social Science Theories

Formal Theories

THEMES

Collaborative Enterprises 3 2

Conflict Resolution 2 2

Collaborative Reasoning 3 6

Organization of Knowledge 3 3

Collaborative Complexity 4 5

Spatiotemporal Challenges 6 0

Table 3 outlines the articles relative to the themes and methods. Appended case studies

provided some empirical validation of prevailing design and modeling efforts (e.g., [21, 101,

105]). However, proper experimental findings were scarce (e.g., [57, 86, 114]).

Table 3 The theme-method classification23

METHODS

Modeling & Simulation

Design Research Experiments & Quasi-

experiments

Surveys Cases & Ethnography

THEMES

Collaborative Enterprises 7 5 0 1 2

Conflict Resolution 5 4 1 0 7

Collaborative Reasoning 8 1 4 1 1

Organization of Knowledge 10 3 4 0 6

Collaborative Complexity 19 8 0 1 8

Spatiotemporal Challenges 6 4 1 0 3

4. Research Themes

4.1 Collaborative enterprises: Advances have already been made in the architecture of

collaborative enterprises [22, 23, 53, 83, 84, 111]. Standardized decision protocols can ensure

efficient demand and capacity sharing in collaborative enterprises [127]. They can ensure

conditions for agents to profit from joining or splitting forces [128]. Emergent decision

protocols can improve quality of collaboration [35, 36, 37, 92, 94].

4.2 Conflict resolution: Agent heterogeneity can aggravate agents’ conflicts [75, 114,

1 The total number does not exceed thirty-nine because a number of studies employed identical theories. 2 The cell entries correspond to the frequencies of studies. 3 The total number exceeds eighty-two because a number of studies employed multiple methods.

34

124]. A structured step-wise approach has been proposed to manage agents’ conflicts [109].

Supportive designs profile and statistically merge conflicting decision criteria [91, 113].

Iterative multi-attribute classification can aggregate agents’ preferences into a collective

solution [91, 101]. A distance between agents’ preferences and a collective solution, as well as

a solution acceptance rate, can further strengthen consensus measures [21, 74]. Agents’

capacity to decompose problems and exchange resources defuses conflicts [75, 114, 124].

4.3 Collaborative reasoning: Argumentation is the primary means of integrating

knowledge in collaborative decision-making [40, 41, 63, 106]. Different reasoning frameworks

support argumentation [63, 66, 95]. Agents can reason deductively, sequentially exchanging

premises, demonstrably reaching valid conclusions [63, 66]. They can also reason defeasibly,

non-demonstrably reaching contingent conclusions [95, 122]. Any proposed reasoning

framework only provides assessments of the various positions at any stage of a discussion,

ultimately resolving conflicting interpretations [95]. However, agents also have to agree on a

measures that resolves them [95]. Existing measures can assess collective solutions for

credibility of arguments [11, 12]. Efficiency of measures that profile opinion factions [13, 14,

15] and capture a decision rationale [11, 12] has also been demonstrated.

4.4 Organization of knowledge: Semantics convey function, data, execution, quality,

and trust in collaborative decision-making [44]. Apart from eliciting knowledge [39], agents

have to share and manage knowledge [49, 60, 68, 105, 121, 132]. Knowledge sharing

consolidates aggregate understanding, ensuring commensurability of reasoning frameworks

and efficiency of search for consensus [10]. Knowledge sharing can also result in false

outcomes [46]. In response, clearly specified, shared knowledge representations improve

efficient reasoning [17, 31, 78]. A deeper form of knowledge acquisition [47], an ontology

supports such representations [34, 73, 102]. Formation of ontology in collaborative decision-

making has been merely acknowledged [119].

4.5 Collaborative complexity: Collaborative complexity can be traced to the nature of

decision problems, the number of decision criteria, and the multiplicity of agents [2, 3, 5, 21,

28, 50, 55, 58, 75, 82, 114, 118, 124]. While uncertain problems make groups smaller and

closely knit, complex problems make them greater and loosely organized [53]. Complexity of

decision problems more easily leads to conflict resolutions [75, 114, 124]. By retrieving

information, simultaneously applied multi-attribute methods can contribute to efficiency of

collaborative decision-making [76]. However, rather than complete information [3, 61], agent

network properties and problem decompositions are more likely to achieve this efficiency [22,

35

92, 123, 130].

4.6 Spatiotemporal challenges: Metadata reveals spatiotemporal effects on

collaborative outcomes [65, 98, 99, 115]. Agents’ geographical dispersion, average spatial

distance, and mutual isolation detract from collaborative decision-making [88]. Agents use

maps to evaluate positions rather than to structure a problem they have to solve [57, 58, 87].

Time variably impacts agents’ decision-making [19, 29]. Distributed computing can

dynamically reframe agents’ spatiotemporal configurations [106, 117, 120, 131]. Being subject

to decision-making [19, 57, 87, 97], spatiotemporal effects can also give rise to reverse

causality issues. Thus, such effects on collaborative decision-making remain mixed [33, 72,

112, 126]. Table 4 outlines our themes.

5. Research Directions

Theoretical accounts of collaborative decision-making are scarce. Only fourteen

reviewed studies were confirmatory analyses. Only thirty-nine reviewed studies employed

some theoretical frameworks (e.g., [21, 39, 40, 55, 92]). Only four reviewed studies were

theoretical advances [48, 84, 89, 90]. The need for a cogent theory of collaborative decision-

making could not be more obvious. Our research directions are intended to motivate such

research efforts.

Our first research direction is to identify problem-solving conditions that require

collaborative solutions. In collaborative decision-making, an agents’ collectively owned hybrid

option has to be more valuable than any pre-conceived alternative [89]. It has recently been

acknowledged that such an option has to emerge from agents’ shared knowledge [89].

Table 4 Summary of the key threads

1. A range of standardized and emergent protocols were designed to support collaborative enterprises.

2. Specific techniques aid agents’ conflict resolution that also depends on behavioral variables.

3. Reasoning frameworks that integrate shared knowledge in collaborative decision-making interact with opinion factions’ dynamics.

4. Shared knowledge representation ensures reasoning efficiency, as required by the integration of knowledge in collaborative decision-making.

5. Rather than complete information, specific network agencies and problem decompositions are more helpful in achieving collaborative efficiency.

6. Spatiotemporal effects on collaborative decision-making are mixed.

36

Therefore, we propose to analyze agents’ decisions, by identifying conditions in which a

solution to a decision problem requires this emergence.

Our second research direction is to examine reasoning limits in collaborative decision-

making. Specifically, an ill-structured decision problem is characterized by undecidability,

uncertainty, and complexity, i.e., reasoning limits [25, 129]. Agents’ knowledge exists and is

integrated into collaborative decision-making under reasoning limits. These limits can be

variably specified by argumentation logics. They may provide yet another perspective on the

emergence of an agents’ collectively owned hybrid option. Thus, we propose to examine how

agents’ reasoning limits bound this emergence.

Our third research direction is to examine a dependence of collaborative solutions on

spatiotemporal configurations. The dependence of collaborative decision-making on

spatiotemporally configured agents has to be cogently explained [19, 33, 57, 72, 87, 88, 97].

The emergence of a collectively owned hybrid option from agents’ shared knowledge will

remain central to this dependence. Therefore, we propose to examine this dependence by

explaining how agents’ spatiotemporal configurations bound this emergence.

Our fourth research direction is to profile collaborative failures for mixtures of agents’

behaviors. “In collaborative decision-making, we do not strive for an optimum, a compromise,

or a satisficing solution” ([89], p. 1). Manipulative [110] and error reducing [4, 16] behaviors

are also non-collaborative. Thus, we propose to profile collaborative failures for mixtures of

such non-collaborative behaviors.

Our fifth research direction is to embed all of the above considerations into the

architecture of collaborative enterprises. We propose to study how to design these

considerations into collaborative enterprises [22, 23, 130]. We also propose to study their

effects within decision protocols intended for collaborative enterprises [35, 36, 37, 127, 128].

Table 5 outlines our research directions.

Table 5 Summary of the proposed research directions

1. Identify problem-solving conditions that require collaborative solutions.

2. Examine collaborative decision-making for implications of agents’ reasoning limits.

3. Examine collaborative outcomes for spatiotemporally configured agents.

4. Profile collaborative failures for mixtures of agents’ non-collaborative behaviors.

5. Embed the above effects into the architecture of collaborative enterprises.

37

In conclusion, our review is a milestone for the research of collaborative decision-

making. By making emergent, collectively owned hybrid options a central consideration in

collaborative decision-making, our directions can guide this research towards a cogent theory.

They can also encourage related empirical advances.

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