Conceptos en MDA

Introduction

The first chapter discussed the field of online deliberation. It observed how researchers from political science and deliberative theory backgrounds had explored the potential for ICT and online communication to deepen democratic processes and support large scale deliberation. Within the field of computer science, it is possible to observe related discussions taking place regarding how ICT and online communication may support decision making and collective action, or how technology can be used to utilise shared knowledge for the common good. These discussions may be associated with a number of related terms, “collective intelligence”, “computer supported cooperative work” (CSCW), “group decision support systems” and “human computer interaction” have all been used to identify work in computer science relevant to online deliberation.

Collective Intelligence has been broadly defined as multiple agents acting in a way that seems intelligent or achieves goals (Salminen 2012, Malone 2012). In its broadest conceptualisation the agents need not be humans, and the agents involved need not be aware of the goals that are being achieved during the process (Malone 2012, Couzin et al 2005). Discussions of collective intelligence often refer to the notion of the “wisdom of crowds”, and the famous observation of Francis Galton; during a livestock fair, villagers and experts are invited to guess the weight of an ox, it is found that the average of the villagers’ guesses is more accurate than the estimates of individual experts (Malone and Bernstein 2015). The phenomena of the wisdom of crowds can be contrasted with the observation of the “madness of mobs”, circumstances where groups perform badly, reaching incorrect or unjust decisions, failing to utilise individual expertise due to a variety of potential factors including systematic bias and “group think” (Lo 2015). Malone (2012) describes the study of collective intelligence as trying to understand how to foster collective intelligence (as opposed to collective stupidity), and the conditions under which collective intelligence can be improved, and the tasks it can help support. Collective intelligence

has included the development of a measure of “collective IQ” and methods for studying and testing group performance in different contexts and considering different variables. Of particular importance in discussions of collective IQ is the role of emotional intelligence, including the ability to understand non-verbal cues and facial expressions (Cherniss 2010, Malone and Bernstein 2015, Malone 2012). A significant area of focus for the study of collective intelligence has been within the context of computer science and human computer interaction. Douglas Englbert has been credited as an early advocate of collective intelligence in the context of the use of ICT to “augment the human intellect” (Englebert 1995). Collective intelligence is often discussed specifically in terms of ICT supported human collaboration (Malone and Bernstein 2015, Smith 1994, Heylighen 1999, Levy 1995). In this respect, collective intelligence overlaps with other areas of study in computer science, notably human computer interaction (HCI), computer supported cooperative work (CSCW) and group decision support systems (GDSS). Further writers have highlighted the relevance of discussions of collective intelligence to democracy and political science, for example Pierre Levy (1994, 2005) and Heylighen (1999). Heylighen defines collective intelligence as “the ability of a group to find more or better solutions than would be found by its members individually” and transforming the web into a “global brain” or “collective mental map” (Heylighen 1999:253). Pierre Levy (2005) discusses the project of cyber-democracy and using ICT to support ambitious and novel forms of governance.

Collective Intelligence has been influential in the development of various online deliberation platforms (for example Climate CoLab, Debategraph, Deliberatiorium and Cohere, all discussed later in the thesis). The field has introduced a variety of approaches to understanding, evaluating and supporting deliberation and decision making, drawing on various disciplines, including research into organisational theory, social psychology and cognitive research. Collective intelligence literature does not exclusively deal with ICT or humans in contrast with CSCW, HCI and GDSS. HCI does not exclusively deal with collaborative work and includes ICT use by individuals in contrast with CSCW and GDSS. Where these fields do deal with groups of humans

collaborating through ICT there is much overlap, they share similar theoretical influences (for example the work of McGrath (1984) informs CSCW (Liebe et al 1995), GDSS (Antunes and Ho 2001) and collective intelligence (DeSanctis and Gallupe 1987, Grudin and Poltrock 2012) research, and there is cross over with researchers in the field (for example Malone writes of collective intelligence in CSCW (Kim et al 2017). Given the focus of online deliberation presumes human groups collaborating using ICT, the broad differences between collective intelligence and the other fields are not particularly pertinent to the discussion. Nevertheless, our capacity to bring together these developments and consider how they relate to deliberative democratic theory is complicated by a number of factors.

In comparing collective intelligence and related literature to deliberative theory it is helpful to observe the following points of contrast. The field of collective intelligence approaches the issue of decision making with a different set of conceptual tools and concerns, and there is much disagreement within the field on approaches to decision making. Indeed Salminen (2012) describes the field of collective intelligence as being in danger of fragmentation. The context in which decision making takes place may differ from the focus typical of online deliberation, for example collective intelligence and related literatures often focus on decision making in organisations and the private sector (Malone and Bernstein 2015). This raises the question of how relevant these discussions are to the context of deliberation in the public sphere or in the context of public participation in debates on policy making. Secondly, the method of coordination may not rely on deliberation; for example, even in cases where collective intelligence is focused on issues of politics and policy making, certain examples of crowd sourcing do not involve deliberation as a coordination method per se (for example Sadilek et al 2012). This raises the issue of how deliberation as a form of decision making may relate to other forms of knowledge coordination. This chapter provides a theoretical exploration of the field of collective intelligence and its relationship to deliberative theory and online deliberation. The first section outlines key themes and concepts in the collective intelligence literature. The second section explores how these approaches relate to deliberative democratic theory and

the challenges of online deliberation. The chapter observes that the field of collective intelligence shares many common areas of interest with deliberative democracy and provides potential insights into deliberative decision making. The chapter argues that collective intelligence literature provides a range of categories and taxonomies for thinking about the epistemological tasks and processes involved in decision making, and that this reveal ways in which deliberative theory is under-defined and highlights challenges that may benefit from further attention. It is argued that collective intelligence is particularly useful in identifying conditions for successful collective decision making and sources of inefficiencies or distortions such as cognitive bias or information flow problems in deliberation. Furthermore, this area of research provides techniques and tools for addressing these issues, and engages with practical concerns regarding how ICT and design can be used to foster collective intelligence.

Concepts and Themes in Collective Intelligence

Collective Intelligence in Groups and Organisations

An important area of influence on collective intelligence and related literatures is organisational theory and approaching issues of collective intelligence by seeking to better understand the features and dynamics of groups collaborating to achieve particular goals. Researchers have talked about groups and group decision making in different ways, appealing to different frameworks and understandings (McGrath 1984). This section will highlight two influential approaches, McGrath’s development of a conceptual framework for the study of groups and classification found in Groups: Interaction and Performance (1984) and Galbraith’s five point “STAR” model. Applied to deliberative processes, these models suggest ways in which we might helpfully identify different elements or stages of a given process to better understand sources of inefficiency or issues of epistemological or democratic concern.

McGrath’s Groups: Interaction and Performance

McGrath’s (1984) discussion of groups and classification of group tasks has been influential in collective intelligence and related literature (DeSanctis and Gallupe 1987, Grudin and Poltrock 2012). In the CSCW literature it has provided a framework

for thinking about how ICT can support different types of tasks (Grudin and Poltrock 2012).

McGrath (1984:12-13) begins his discussion by identifying a series of variables relating to groups. He identifies the following four general variables

Individuals: variables relating to the traits and characteristics of the individuals that form the group, for example age or gender

Group Structure: variables at the level of the group, for example the size of the group, whether the members like each other, how long they have known each other, relations between members (for example whether some exhibit dominance over others)

Environment: variables concerning where the group activity is performed, examples provided include the workplace, a conference, a family dinner table.

Tasks: variables relating to the assumed goals of the group and the roles and jobs ascribed to the participants

McGrath (1984) then elaborates an account of the interaction process to identify further elements of communication. He identifies three aspects of communication; communication (behaviour of an individual, verbal or otherwise), communication process (the series of behaviours exhibited by the group), communication pattern (the form and structure of that behaviour). McGrath also breaks down the communication in relation to its task orientated component and its interpersonal component. The thought is that the content of communication has some component that relates to achieving the task, and some component that relates to interpersonal relations within the group. Since he is attempting to provide a framework for the study of groups, he also identifies factors for consideration when exploring studies into groups; generalisatibility (from agents to the general population), precision (internal validity), and realism (external validity). The most influential aspect of McGrath’s work on collective intelligence and related literatures is his discussion of the typology of tasks.

McGrath (1984) provides a review of prior approaches to categorising tasks, identifying that tasks may be classified on any of several different bases; the physical environment, behaviours required by the task, behaviours usually elicited by the task (e.g creativity or discussion), relations among the behaviours of individual group members (e.g competition, cooperation, individual effort), classification in terms of goals or criterion (e.g minimising errors, increasing speed) (McGrath and Altman 1966 in McGrath 1984:55). McGrath (1984) attempts to combine these previous efforts into one general scheme, represented below:

(McGrath 1984:61)

McGrath also provides the following table further explaining this system of classification:

Quadrant 1: GENERATE

Type 1 Planning Tasks: Generating plans. E.G: Hackman’s “problem solving”. Key

Notion: Action-oriented Plan

Type 2 Creativity Tasks: Generating ideas E.g Hackman’s “production” tasks,

“brainstroming” tasks. Key notion: Creativity Quadrant 2: CHOOSE

Type 3 Intellective Tasks: Solving problems with a correct answer. E.g Laughlin’s

intellective tasks , with correct and compelling answers; logic problems and other problem solving tasks with correct but not compelling answers; tasks for which expert consensus defines answers. Key notion: Correct answer

Type 4 Decision-Making Tasks: dealing with tasks for which the preferred or

agreed upon answer is the correct one. E.g tasks used in rishy shift, choice shift, and polarization studies; juries. Key notion: preferred answer Quadrant 3 NEGOTIATE

Type 5 Cognitive Conflict Tasks: Resolving conflicts of viewpoint (not of interests)

E.g Cognitive conflict tasks used in social judgement theory work; some jury tasks. Key notion: Resolving policy conflicts

Type 6 Mixed-motive Tasks: Resolving conflicts of motive interest E.g

negotiations and bargaining tasks, mixed motive dilemma tasks, coalition formation/reward allocation tasks. Key notion: Resolving pay off conflicts Quadrant 4 EXECUTE

Type 7 Contests/Battles: Resolving conflicts of power; competing for victory E.g

wars, all winner-take-all conflicts, and competitive sports. Key notion: Winning

Type 8 Performances: Pyschomotor tasks performed against objective or

absolute standards of excellence, e.g many physical tasks, some sports events. Key notion: Excelling

McGrath (1984:62)

McGrath’s framework for groups and group tasks has provided an influential way for researchers to talk about groups and how ICT might support the performance of groups in relation to different task types. For example DeSanctis and Gallup (1987)

apply this framework to different forms of ICT support in GDSS, summarised in the table below.

Task Purpose Task Type Possible Support Features

GENERATE Planning Large Screen Display, graphical aids Planning tools (e.g PERT)

Risk assessment, subjective probability estimation for alternative plans

Creativity Anonymous input of ideas; pooling and display of ideas; search facilities to identify common ideas, eliminate duplicates

Nominal Group Technique, Brainstorming CHOOSE Intellective Data access and display; synthesis and display of

rationales for choices

Aids to finding the correct answer (e.g forecasting models, multiattribute utility models)

Rule Based Discussion emphasising thorough explanation of logic

Preference Preference weighting and ranking with various schemes for determining the most favoured alternative; voting schemes

Social judgement models; automated Delphi Rule Based discussion emphasising equal time to present opinion

NEGOTIATE Cognitive conflict

Summary and display of members’ opinions Using Social Judgement Analysis, each member’s judgements are analyzed by the system and the used as feedback to the individual member or the group Automatic mediation; automate Robert’s Rules Mixed Motive Voting Solicitation and Summary

Rule base for controlling opinion expression: automatic mediation

DeSanctis and Gallupe (1987:601) This discussion has provided a brief outline of McGrath’s work on group dynamics, and an example that illustrates how the framework has been used to talk about ICT systems and their support for decision making. The next section will consider Galbraith’s STAR system, as an alternative approach to talking about organisations and collective intelligence.

Galbraith’s STAR system

An influential framework for understanding the coordination of teams and organisations has been Galbraith’s five point “STAR” model, developed in the 1960s (Galbraith 2002). Although Galbraith applies his framework to businesses and formal organisations, the framework has been used in the literature to talk about collective intelligence more broadly (for example Woolley et al 2015). Galbraith’s (2002:72) star system emphasises the following:

Strategy: what the team is trying to accomplish

Structure: how agents coordinate and decision making power Processes: flow of information in the group

Rewards: motives and incentives for desired behaviour

Right people: how to ensure people are best allocated to the work, and potential and expertise are being used.

Strategy

Strategy incorporates the nature of the task and the goals that the group are trying to accomplish. Woolley et al (2015) suggest there are two broad sources of failure at this point, if the task is not suited to collective work, or the goals and objectives are unclear. How we conceptualise group tasks depends on our framework and the kind of area of collective intelligence we are interested in. McGrath’s (1984) framework

of group tasks discussed above has been frequently drawn on in the literature (Woolley et al 2015), other examples include Steiner (1966, 1972) who categorises task types in relation to group performance;

Conjunctive: the group performs to the lowest of individual ability (e.g running in a group)

Disjunctive: the group performs to the best of individual ability (e.g solving a maths problem)

Additive: all contributions add to a group performance (e.g collecting a resource) Compensatory: performance of others compensates for failings in individual performance (e.g the wisdom of crowds and collective estimates).

Structure

Structure describes the placement of power and authority in organisations (Galbraith 2002) and the way in which agents coordinate to make decisions (Woolley et al 2015). Faraj and Xiao describe coordination in organisations as “the integration of organisational work under conditions of task interdependence and uncertainty” (Faraj and Xiao, 2006:187). They observe how research has emphasised the distinction between formal and informal modes of communication, and the need for informal coordination in conditions of uncertainty. Woolley et al (2015) illustrate this with the use of tacit coordination and on the spot information sharing in the context of laboratory teams and medical emergency units.

At the macro level, hierarchies and markets are two common ways in which groups coordinate. Collective intelligence literature has explored collective intelligence in markets (Lo 2015), while Woolley et al (2015) observe the majority of literature has focused on collective intelligence in the context of the structure of hierarchies. Under a hierarchical structure coordination is organised vertically, with decision making power residing at the top of the structure and filtered down through various layers of management. The literature also divides the issues of structure into differentiation and integration; differentiation involves dividing labour, for example dividing an organisation into different departments. There is then an issue of organising coordination between different departments, for example setting up casual

meetings, formal meetings, or matrix managers to oversee coordination between groups. Integration is described as the management of dependencies (Woolley et al 2015). Thompson (1967) describes three types of interdependencies: pooled (shared resource, such as money or a machine), sequential (where work relies on the completion of other tasks), reciprocal (where resources flow back and forth). In addition to discussions of hierarchy and market coordination, there is increasing interest in the concept of “networks”, for example in Neither Market Nor Hierachy Powell (1990) argues that networks, understood as informal and shifting connections within organisations and between different organisations, are more important for understanding coordination in social systems than hierarchies.

Process

Processes describes the flow of information within an organisation or group engaged in a task; this includes considerations such as how well a group can learn and how well groups can rely on the knowledge of other members. Woolley et al (2015) suggests that the elements of process most germane to the study of collective intelligence are those which characterise intelligence systems more generally, identifying these as memory, attention and problem solving. Factors related to problem solving encompass many of the issues discussed under group tasks, and factors relating to cognitive processes and cognitive bias discussed in the following section.

A key theme in collective intelligence literature is memory in groups; Salminen (2012) suggests that the concept of the internet as a shared memory of humankind has been mentioned repeatedly (e.g. Levy 2010, Luo et al. 2009, Heylighen 1999 in Salminen 2012). Wegner (1987) discusses the notion of group memory, and describes a transactive memory system (TMS) as the capacity to store and retrieve information in different domains. He identifies three behavioural indicators of TMS; specialisation, credibility and coordination. Specialisation is reflected in how the group members divide cognitive labour tasks, credibility is reflected in members’ reliance on one another to be responsible for specific expertise, such that they

possess all information needed for their tasks, and coordination reflects how smoothly and efficiently such knowledge is exchanged and called on.

Alternatively, the issue of process has also been discussed in the context of attention in groups (Ocasio 2011). Ocasio (2011) presents an attention based view of the firm. In light of increasing availability of information, he observes that there remains a scarcity of attention, and in this sense our focus should be on theorising around attention in groups. Although he places his discussion within the context of business, he believes his arguments apply equally to other group behaviours such as the response of governments to threats such as terrorism. He proposes three principles: “Focus of attention”, what are the issues that firms pay attention to. “The principle of situated attention”, the thought that attention is situated within time and space, and the organisation of an individual’s attention on different things at different times, and problems that arise from inconsistencies or shifts in attention. “The structural distribution of attention” the firm’s formal and informal structure shapes how attention is focused. Ocasio (2011) identifies four determinants of attention in relation to formal and informal structure; the rules of the game, the agenda of the