Exercising our Freedom and Intelligence: Part 9

dialogue-1In his book, Societal Systems: Planning, Policy and Complexity, John Warfield (1976) — motivated by our inability to resolve societal problems — focused on developing methods to support our collective intelligence. Although Warfield was writing 40 years ago, in 1976, his words resonate today, in 2016:

“Examples of important societal problems abound – wars, crime, poverty, urban problems, regional problems, international problems, inflation, malnutrition, starvation, and disease. Experience shows how imperfectly we deal with these problems…Shortages impend in energy, food, water, affection, wilderness, knowledge, personal freedom, and wisdom. Excesses impend in pollution, population, crime, hatred, war, ignorance, and human suppression…Societal problems, being interlocked, challenge human ingenuity” (p. 1 – 3).

Facilitating collective intelligence is a subtle, fascinating, and incredibly challenging and rewarding activity. Groups working to maximize their collective understanding and problem solving ability need a space where they are free to exercise their intelligence, and they also need sound methodologies to help them synthesize their knowledge and maximize the power of their collective intelligence. I’ve worked with John Warfield’s methodology, Interactive Management, for the past seven years now. It’s a very useful methodology for both basic and applied social science research and it has been central to work we have done on critical thinking, music listening, facebook usage, technology design for literacy, technology design for democracy, national well-being measurement, and much more. I’m currently writing a book, Facilitating Collective Intelligence, where I expand upon Warfield’s vision for applied social science. Freedom is central to my vision.

Warfield was a methods man and a consummate genius. He didn’t write about freedom, but I believe that freedom permeated his worldview. Freedom is something we all value and I argue that it needs to be built into the social infrastructure that supports our collective intelligence. Given the political nature of our societal problem solving activity, it’s important to develop a political philosophy that guides our work together. I’m hugely impressed by the clarity and power of Philip Pettit’s political philosophy, which is fundamentally grounded in a unique perspective on freedom (Pettit, 2014). While many people define freedom as the absence of interference – we are left alone to do as we please – Pettit proposes a much more subtle and reflective and relational model of freedom. Pettit argues that, in their basic life choices free persons should not be subject to the power of others – they should not be subject to a power of interference on the part of others; they should not be dominated by others. Politics isn’t about leaving people alone per se – at its best, it’s about working with people to make collective decisions. But when we work with other people, we should not work to dominate them. This principle of freedom as non-domination provides a simple, unifying standard for evaluating social and democratic progress, says Pettit. It also provides a basis for progressively redesigning our approach to political decision-making, and for analyzing the political decisions we make. As noted by Pettit, freedom as non-domination, an ideal that was central to the Roman Republic, implies a free citizenry who enjoy equal status with one another, being individually protected by the law that they together control. It is a powerful principle with implications for social, political, and international justice. It is also a principle upon which to build an approach to applied social science that upholds social, political, and international justice. We can synthesize Pettit’s philosophy and Warfield’s method within an approach to applied science that helps us to collectively resolve societal problems.

Freedom as non-domination is a principle that implies a specific approach to the practice of communication, meaning-making, and problem solving in a group. As everyone in the group is empowered, and explicitly works to uphold the power of everyone else, their communication and interaction reflects this principle in a dynamic practice of engagement. Indeed, outside of the specific rules, infrastructures, technologies, and artefacts of culture that we may design specifically with freedom as non-domination in mind, our everyday communication and interaction are the primary means through which we exercise our collective freedom as non-domination. Communication in this context manifests in the form of a dialogue where everyone is equally empowered, not a series of monologues where people hold on to some modicum of power for a time before another person ‘takes to the stage’. Indeed, in the monological tradition to communication and ‘group learning’, some people may never even make it onto the stage. Genuine group learning requires that everyone is involved – everyone is on stage, much like a choir performing in unison. Unlike the long monologues of the scholars at academic conferences (and the often-humorous antipathy to questions and discussion after the long monologue); and unlike the long, berating monologue of the teacher in the classroom followed by an abrupt response to a student who has a question, enacting the principle of freedom as non-domination in a group problem solving session implies a dialogue, not a series of frustrated monologues and brief divisive discussions.

The study of dialogue has a long history in psychology, communication, management, education, and philosophy. In his recent essay on dialogue, Broome (2009) reviews the major thought leaders who influenced the study of dialogue. The English word dialogue comes from the Greek word “dialogos,” and it implies that meaning (logos) is prefixed or arises through or across (dia) the communication at the group level (Broome, 2009). As such, dialogue implies a synthesis of meaning that emerges at the group level, and it implies that the group is somehow unified in this effort. In order to achieve this unity of effort and synthesis of meaning, the group needs to adopt a principled stance and communicate in a way that reflects this principled stance. Naturally, learning how to engage in a dialogue requires some practice and facilitation and it may take some time for a group to develop their dialogue skills. Freedom as non-domination can serve to frame a move from monological to dialogical communication and from self-centered to team-centered activity. This is consistent with a number of theoretical views on dialogue (see Figure 1).

 
dialogue

Figure 1. Theoretical views on dialogue

A number of classic models of dialogue are noteworthy in this regard. First and foremost, the practice of dialogue involves a unique way of perceiving other people.  As classically described by Buber, there is a move from seeing people as ‘objects’ to be persuaded, manipulated, or dominated in some way (i.e., where people are dominated by an I-Itperception of their relationship with others), to a state of being and perception where people see others as people, much like themselves (i.e., people enter an I-Thourelationship with others).  As such, there is a move away from self-centeredness and any effort at deception, pretense, and domination, and a move away from communication marked by efforts at persuasion, maneuvers to gain prestige, and power-plays to control others in the exchange. We move instead towards a more genuinely communicative state, marked by genuine listening, honesty, spontaneity, directness, and mutual responsibility. Communication is no longer a competition with one winner, a discussion where only one person emerges as powerful and correct, or a conflict where the victorious vanquish their victims. Instead, communication serves to build the relational power and meaning and dialogic intelligence of the whole group, and everyone in the group.  As noted by Broome (2009) this view “gives recognition to the interdependence of self and other, the intersubjectivity of meaning, and the emergent nature of reality.” (p. 2).

Carl Rogers emphasized that the interdependence of dialogic relationships also requires a unique concern for human feelings, human relationships, and human potential. Rogers developed a view consistent with the principle of freedom as non-domination: he emphasized the importance of empathy and careful listening, and cultivating a genuine trust in the wisdom of human beings. As noted by Broome (2009):

“He encouraged stripping away facades and moving away from “oughts,” expectations of others, and attempts to please others. Rogers believed that a space could be opened for dialogue when relationships are characterized by a willingness to listen and to enter into a meaningful relationship with the other, genuineness in sharing feelings and ideas with the other, respect and regard for the other, and empathic understanding, which he viewed as entering the private perceptual world of the other and becoming “at home” in it.” (p. 2).

Building upon the principled stance of Buber and the empathic ground of Rogers, Gadamer noted that it is through language that understanding is built in dialogue.  Language and emerging understanding clearly manifest in a dialogue as a living, dynamic process that is open to continual development and change as people continue to engage with one another.  People come to a dialogue with unique prior knowledge, understanding and prejudices, and the context within which the group engages with one another is always unique.  Prejudice, or the various assumptions and biases of individual group members, comes to be recognized and understood as a feature of communication, which forms the basis for deeper understanding as a fusion of horizons develops between members of a group engaged in dialogue. Ultimately, says Gadamer, a “higher universality” emerges that overcomes the limited horizons of each participant. This is a view consistent with the principled methodological approach to collective intelligence developed by John Warfield, in the sense that, in a structured dialogue, thinking develops from the separate positions of individuals to a synthesis that combines individual views.

This is consistent with the view of Bakhtin, who noted the need to balance any emerging dialogic synthesis and common understanding with the uniqueness of individual perspectives. This implies a certain tension in the fluid, open, dynamic dialogic interaction, where, as Broome (2009) notes:  “there is a dynamic interplay of expression and non-expression, certainty and uncertainty, conventionality and uniqueness, integration and separation…an emergent process in which the interplay of contradictory forces creates a constant state of unrest and instability, while also bringing moments of unity and synthesis.” (p. 3). John Warfield’s effort to develop a methodology and technology to support collective intelligence was designed to produce more than mere ‘moments’ of unity and synthesis – it was designed with the specific intention of producing a synthesis that makes concrete key aspects of the group’s collective intelligence in the form of graphical and linguistic products that showcase the synthesis of language and logic generated by a group during a structured dialogue.  At the same time, Warfield recognized that the process of developing these enduring, consensus-based products entails a dynamic process which requires careful facilitation of dialogue in the room. Consistent with Böhm’s view on dialogue, participants in a collective intelligence session need to be patient with the facilitator and with one another; they need to suspend judgement in relation to their own and others’ beliefs and opinions, thus allowing a variety of perspectives to co-exist in tension, without premature attempts to resolve them or achieve a ‘quick synthesis’ at the expense of a fuller, deeper synthesis.  It is the fuller, deeper synthesis and more coherent understanding of a problematic situation that sustains the work of the group into the future.

Consistent with Paulo Freire, it is important to ground our ongoing collective intelligence work at a societal level with a solid foundation of dialogic education– we need to learn very early in life, and throughout our lifelong education, how to engage in dialogue and how to learn through dialogue.  We need to learn how to protect the dignity of learners, allowing for exploration of new ideas without fear of humiliation. We need to learn how to affirm others in this dialogic learning process, says Freire, and help to instill hope in the minds of an otherwise oppressed community.  Indeed, we remain oppressed to the extent that we inhibit dialogue and collective learning and rely instead on the authority of others and their monological wisdom.  As noted by Broome (2009, p. 3), in this view:

“Dialogue is built on humility to learn from the other, guided by trust between communicators, and pushed forward by hope for liberation from oppression.”

Dialogue, in this view, allows us both to challenge forms of domination that result in oppression and fashion together a new scenario for our future.  Dialogue is more than just idle chatter: it is a form of action that inspires change that helps to transform our world.  Of course, in order to transform our world for the better through dialogue, we need to perform well as a group.  As Warfield hoped for, our collective intelligence should inform effective collective action, whatever this means for the group in context of their local problematic situation.  It can help to gain some perspective on this issue by examining some of the recent empirical literature on key aspects of effective collective intelligence and how dialogue, in its fullest sense, might help to support these outcomes.

Beyond ‘the talk’ of dialogue – searching the deeper aspects of collective intelligence

In a recent paper, Wegerif and colleagues (2016) highlight a number of interesting theoretical and empirical issues in relation to dialogue and collective intelligence.

First, we can happily report that Paulo Freire’s call for more dialogue in educational practice has not fallen on deaf ears, and many studies have investigated the effects of classroom dialogue on educational outcomes (Howe and Abedin, 2013).  However, many of these studies begin by proposing a model of good dialogue and then they work to assess the impact of their educational intervention on key measurable aspects of this model. Wegerif and colleagues draw our attention to many different models of ‘effective talk’ for group thinking, including: Accountable Talk (Michaels, O’Connor, & Resnick, 2008), Exploratory Talk (Mercer & Littleton, 2007), Progressive Enquiry (Muukkonen, Lakkala, & Hakkarainen, 2009), Quality Talk (Davies & Meissel, 2016) and Collaborative Reasoning (Resnick & Schantz, 2015).  These models assume a priori that certain features of group talk are more ‘effective’ than other features. But there is a major problem with intervention studies that exclusively use outcome measures derived from these models – specifically, unless one measures group-level performance outcomes, there is no way to know if any increase in ‘effective talk’ is related to an increase in group performance or the overall efficacy of group thinking.  It’s not enough to say there was ‘more effective talk’ observed in the classroom – one needs to evaluate the group-level product of this talk.  One needs a measure of overall group performance in these studies if one is to assess any proposed link between effective talk and group performance outcomes.  Similarly, while dialogue and a push for quality talk in the classroom may have positive effects of individual learner outcomes, as indicated in a recent meta-analysis (Davies and Meissel, 2016), these studies say nothing about the impact of dialogue on collective or group-level thinking and performance outcomes.  Collective intelligence and group-level thinking outcomes are a unique product of group work. If one is focused on enhancing these collective outcomes a specific lens of enquiry and unique group-level performance measures are needed.

At the same time, Wegerif and colleagues remind us of an earlier classroom study they conducted (Wegerif, Mercer, and Dawes, 1999), where two split-half versions of Raven’s non-verbal reasoning matrices were created – one for a group to work on and another for individuals to work on, independent of other students in the class. They found that, compared to a control group who received their usual classroom instruction, an intervention group who were supported to engage in Exploratory Talk showed improvements not only in their individual test performance on Raven’s non-verbal reasoning test, but also on their group-level performance when working with others to solve the puzzles.  These findings suggested that instruction in a specific form of dialogue, Exploratory Talk, may enhance group-level performance in addition to individual student achievement, but the study did not allow for a deeper analysis of the mechanisms through which this intervention worked to enhance performance.  Aspects of the intervention other than the exploratory talk itself may have been instrumental in enhancing performance.

In an effort to examine some key predictors of group-level collective intelligence performance, Woolley and colleagues (2010) made use of a similar individual and group-level Raven’s non-verbal reasoning matrices test, along with other individual and group-level tasks, to examine variation and predictors of group level performance in particular.  Factor analysis indicated that group-level performance across multiple tasks was identifiable as a unique factor or construct which could not be predicted in any simple way by the individual-level performance of group members.  Woolley and colleagues called this factor ‘c’, or collective intelligence, and they found that, much like the individual ability of group members was not a good predictor of ‘c’, measures of motivation, group cohesion, and satisfaction did not predict ‘c’.  But three measures the research team had available to them did predict group-level performance: more equal distribution of turn-taking when talking, the presence of women, and the individual ability of group members to infer emotions from photographs of eyes in the reading the mind in the eyes (RME) test.  Further analysis revealed that the positive impact on group-level performance of having more women in groups was largely explained by the fact that women also scored higher on the RME test.  Indeed, a more recent study by the same team found that higher RME test performance of individual group members predicts higher group-level performance in online tasks, even though group members were not interacting face to face in the online environment (Engel et al., 2015). This is interesting, as it suggests that the ability to infer the underlying emotions and intentions of fellow group members is critical for good group-level performance.

However, Wegerif and colleagues (2016) note that the approach to the analysis of collective intelligence adopted by Woolley and colleagues does not measure the process of dialogue or the nature and quality of group thinking leading to variation in collective intelligence outcomes.  In order to do this, one needs to video record group interactions and code key aspects of those interactions – both verbal and non-verbal.  This is what Wegerif and colleagues did.  Using a similar non-verbal matrix reasoning test, Wegerif and colleagues developed separate individual and group-level tests matched on difficulty.  By comparing group- and individual-level performance profiles, Wegerif and colleagues were able to identify three types of groups: (1) Value Added Groups (i.e., groups that score over one standard deviation more than the highest score of any of the individuals in the group, (2) Value Detracting Groups (i.e., groups that score more than one standard deviation lower than the highest individual performer within the group, and (3) Value Neutral Groups(i.e., groups that score within one standard deviation of the score of the highest individual performer within the group).  Careful analysis of the video recordings, one matrix puzzle at a time, revealed a range of behaviors that characterized successful problem solving and key features of Value Added Groups more generally.  More successful groups engaged in the following behaviors (Wegerif et al., 2016, p. 8):
  • Encouraging each other, for example responding to suggestions with ‘could be … ’
  • Expressions of humility, for example ‘I do not understand this.’
  • Giving clear elaborated explanations, for example ‘the triangle here is removed and here it turns around by 90_’
  • Equal participation with everyone in the group actively involved in each problem.
  • Actively seeking agreement from others, for example by asking ‘do you agree?’
  • Not moving on until it is clear that all in the group understand for example asking ‘I do not understand it, can you explain again?’
  • Open questions, for example ‘can anyone see a pattern here?’ and ‘what do you think?’
  • Warm positive affect with shared smiles and laughter.
  • Willingness to express intuitions, for example, ‘I am not sure but I have a feeling it is that one’
  • Indications of mutual respect in tone and responses.
  • Taking time over solving problems seen in accepting pauses and giving elaborated explanations when asked.

Wegerif and colleagues note that, while many of these behaviors feature in existing models of effective talk noted above, linking these behaviors specifically to better group-level performance is innovative and requires further research.  Notably, a number of the behaviors linked to successful group performance including the use of humor and efforts to express intuitions in the absence of supporting reasoning do not feature strongly in many models of effective talk and thus warrant further theoretical and empirical consideration.

Returning to Warfield’s collective intelligence method, a key outcome of which is a systems model describing a specific issue (see example here), one can immediately see that the nature of the collective intelligence product that Warfield was interested in is very different from the nature of the collective intelligence product both Wegerif and colleagues and Woolley and colleagues are interested in.  However, the same lens of enquiry advocated by Wegerif and colleagues can be applied to Warfield’s method, in the sense that we can examine the impact of key group processes and key group behaviors on overall group performance.  Although the criteria for measuring group performance may be different when we compare the solving of puzzles with the construction of systems models, criteria can be established that are useful for groups to reflect upon.  I will examine some of these criteria in a future blog post.  My closing point for now is a simple one: while key aspects of effective dialogue may indeed support both individual and group-level learning and performance outcomes, the way in which we support and facilitate dialogue needs to cohere with, sustain, and enhance the specific type of collective intelligence outcome we are aiming for.  There are many different types of collective intelligence outcome we might aim for, and we need to know what we are aiming for, why we are aiming for it, and how best to support groups with specific aims.  Facilitating collective intelligence is a subtle and naturally fascinating field of activity.  I believe we are on the cusp of some radical breakthroughs in our understanding and application of collective intelligence. Warfield would no doubt be proud of our progress.

References:

Broome, B. J. (2009). Dialogue Theories. In Steven Littlejohn and Karen Foss (Eds.), Encyclopedia of Communication Theory. Sage

Davies, M., & Meissel, K. (2016). The use of Quality Talk to increase critical analytical speaking and writing of students in three secondary schools. British Educational Research Journal, 42(2), 342e365.

Engel, D., Woolley, A. W., Aggarwal, I., Chabris, C. F., Takahashi, M., Nemoto, K., & Malone, T. W. (2015, April). Collective intelligence in computer-mediated collaboration emerges in different contexts and cultures. In In proceedings of the 33rd annual ACM Conference on human factors in computing systems (pp. 3769-3778). ACM.

Howe, C., & Abedin, M. (2013). Classroom dialogue: A systematic review across four decades of research. Cambridge journal of education, 43(3), 325e356.

Mercer, N., & Littleton, K. (2007). Dialogue and the development of children’s thinking: A sociocultural approach. London: Routledge.

Michaels, S., O’Connor, C., & Resnick, L. B. (2008). Deliberative discourse idealized and realized: Accountable talk in the classroom and in civic life. Studies in philosophy and education, 27(4), 283e297.

Muukkonen, H., Lakkala, M., & Hakkarainen, K. (2009). Technology-enhanced progressive inquiry in higher education. In M. Khosrow-Pour (Ed.), Encyclopedia of information science and technology I-V (2nd ed., pp. 3714e3720). Hershey, PA: Information Science Reference.

Resnick, L. B., & Schantz, F. (2015). Re-thinking intelligence: Schools that build the mind. European Journal of Education, 50(3), 340e349.

Warfield, J. N. (1976). Societal systems : planning, policy, and complexity. New York: Wiley.

Wegerif, R., Fujita, T., Doney, J., Perez Linares, J., Richards, A., & van Rhyn, C. (2016). Developing and trialing a measure of group thinking. Learning and Instruction. In Press

Wegerif, R., Mercer, N., & Dawes, L. (1999). From social interaction to individual reasoning: An empirical investigation of a possible sociocultural model of cognitive development. Learning and Instruction, 9(5), 493e516.

Woolley, A., Chabris, C., Pentland, A., Hashmi, N., & Malone, T. (September 30, 2010). Evidence for a collective intelligence factor in the performance of human groups. Science, 330(6004), 686-688.

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