In part, this chapter provides more detail about the concept of tipping points in relation to the previous chapter Taking Action.
A relatively early book by Malcolm Gladwell on the subject from 2002 (pre-social media) was popular at the time, but lacks specifics. The broad concept is clear but we need specifics which will allow us to generate change rather than watch the after-effects.
The experiment that Centola et al ran was constructed as follows. See the paper online for additional details and the numbered references.
We recruited 194 subjects from the World Wide Web and placed them into online communities where they participated in a social coordination process (27, 28). Upon arrival to the study, participants were randomly assigned to participate in one of 10 independent online groups, which varied in size from 20 to 30 people. In a given round of the study, the members of each group were matched at random into pairs to interact with one another. Within each pair, both subjects simultaneously assigned names to a pictured object (i.e., a face), attempting to coordinate in the real-time exchange of linguistic alternatives (20, 25). If the players entered the same name (i.e., coordinated), they were rewarded with a successful payment; if they entered different names (i.e., failed to coordinate), they were penalized. In each community, individuals interacted with each other over repeated rounds of randomly assigned pairings, with the goal of coordinating with one another (25). Participants were not incentivized to reach a “global” consensus but only to coordinate in a pairwise manner with their partner on each round. Participants were financially rewarded for coordinating and financially punished each time they failed to coordinate with each other (25). Once a convention was established for the entire population, the incentives strongly favored coordinating on the equilibrium behavior.
After each round, the participants could see only the choices that they and their partner had made, and their cumulative pay was updated accordingly. They were then randomly assigned to interact with a new member of their group, and a new round would begin. These dynamics reflect common types of online exchanges, in which community members directly interact with the other members of a large, often anonymous population—using, for instance, chat interfaces or messaging technologies—leading them to adopt linguistic and behavioral conventions that allow them to effectively coordinate their actions with other participants’ expectations (20, 29 ,30). Consistent with these types of settings, participants in the study did not have any information about the size of the population that was attempting to coordinate nor about the number of individuals to whom they were connected (9, 20, 23). In every group, this interaction process quickly led to the establishment of a group-wide social convention, in which all players in the network consistently coordinated on the same naming behavior (20, 25). Once a convention was established among all experimental participants, we introduced a small number of confederates (that is, a “committed minority”) into each group, who attempted to overturn the established convention by advancing a novel alternative (25).
Trials varied according to the size of the committed minority (C) that attempted to overturn the established convention. In total, we studied the dynamics of critical mass in 10 independent groups, each with a committed minority of a fixed size. Across all 10 groups, the sizes of the committed minorities were in the range (15% < C < 35%).
Other research has suggested that a much smaller proportion of a population can generate change - 3.5% - through direct, non-violent, civil protest. However, it is likely that this percentage relates to those most active and that a larger proportion agree but don't directly act.
Regarding the “3.5% rule”, [Chenoweth] points out that while 3.5% is a small minority, such a level of active participation probably means many more people tacitly agree with the cause.
Social ties are useful. However, the stronger they are the more likely you might be to conform to the social norm (or feel pressure to do so). In which case, your reality will be partially shared as a group experience. Doing something different may be harder. But all the more impactful too.
The strength of leadership (doing something different and showing others how to do it) is in being able to make that initial change. Once you have a supporter, it can be surprisingly easy to spread the new idea among a social group.
Indeed, information flow or integration may be a fundamental aspect of consciousness and of group dynamics. The theory essentially says that the level of information integration is proportional to the level of consciousness. Without integration, unconsciousness occurs.
To change someone's mind and get them to engage in your agenda as a leader, you just need to persuade them to listen and then see the benefit to themselves (or their close relatives). Information integration theory then implies that a couple things will happen:
A person gains new information which might generate new ideas
You make progress towards the magic 20% threshold when a group commits to your leadership!
In terms of applying the "information integration theory" of consciousness to the wider scope of teams and society (emphasis mine),
Thomas Malone, director of the Massachusetts Institute of Technology's Center for Collective Intelligence and author of the book Superminds, has recently applied the theory to teams of people – in the laboratory, and in real-world, including the editors of Wikipedia entries. He has shown that the estimates of the integrated information shared by the team members could predict group performance on the various tasks. Although the concept of “group consciousness” may seem like a stretch, he thinks that Tononi’s theory might help us to understand how large bodies of people sometimes begin to think, feel, remember, decide, and react as one entity.
To apply this as a neuroscience theory (or to understanding the physical basis for consciousness) is early days. But the concept of information integration is fundamental to psychology, and the ideas of how groups share and integrate information is equally fundamental to sociology.
A specific hypothesis, testable, is that groups will successfully adopt new knowledge if > 20% of members have done so. This sounds like the sort of theoretical experiments done to explore the 20% concept and could be tested by the practical social experiments run by Thomas Malone.
An example experiment might be to check if roof-top solar panels emerge in clusters (reflecting a local social network) and that the rate of adoption accelerates to full adoption once a particular area (of some definition) reaches 20% adoption. Google maps data may enable this hypothesis to be tested, particularly now they are using their map database to predict which roofs are suitable for solar panel installation.
Another experiment might be to examine the spread of religion. Those that remain with around <20% population converted remain minority faiths. Census data may enable the testing of this hypothesis.
The proportion of a sector with non-sustainability executives taking part in LinkedIn groups associated with climate change. The role and power of sustainability professionals in a business tends to vary considerably, so the presence of such in a company at all doesn't necessarily signify fundamental change. Hence this experiment deals with non-sustainability executives. It may be that LinkedIn data is already available which could test this hypothesis.
Consider the number of businesses in a country or sector undertaking to commit to what are known as Science-Based Targets. There are many targets a business can set, but these are particularly demanding and future-oriented targets dealing specifically with action on climate change. The Science-Based Target Initiative tracks current progress by country and sector and a number of these have exceeded the 20% threshold. It will be interesting to see if their definition of a suitable network (country or global sector) is the right one and whether change will sweep through those already exceeding the 20% threshold.
Each of these hypothesis tests an aspect of social networking - the basis on which the information integration theory rests.
Damon Centola (2018) "Experimental evidence for tipping points in social convention"
David Robson, BBC (2019) "Are we close to solving the puzzle of consciousness?"
Giulio Tononi & Christof Koch (2015) "Consciousness: here, there and everywhere?"
Jin Fan (2014) "An information theory account of cognitive control"
David Engel & Thomas Malone (2018) "Integrated information as a metric for group interaction"
Saul McLeod (2008) "Information Processing"
Stephen Borgatti et al (2018) "Analyzing Social Networks"
Farmer et al (2019) "Sensitive intervention points in the post-carbon transition"
Otto et al (2020) "Social tipping dynamics for stabilizing Earth’s climate by 2050"
Post Carbon Tipping Points: https://www.postcarbontransition.net/about
Farmer et al (2015) early attempt to update economic models: "A Third Wave in the Economics of Climate Change" (paywall)
Kuran (1989) "Sparks and prairie fires: A theory of unanticipated political revolution" (paywall)
Robson, Chenoweth, BBC (2019) "The '3.5% rule': How a small minority can change the world"
Chenoweth (TED video 2013) "The success of nonviolent civil resistance"
Science-Based Targets Initiative (2019) "Raising the Bar: Exploring the Science Based Targets initiative's progress in driving ambitious climate action"
Everett Rogers (2003). "Diffusion of innovations, 5th Edition"
Xie et al (2011) "Social consensus through the influence of committed minorities" (paywall) Physicists applying network theory show that a committed (unwavering) randomly distributed 10% of a population with a new opinion can change the opinion of the whole population.
Castellani & Hafferty (2009) "Sociology and Complexity Science", starting to think how complexity theory, agent-based or cellular-automata can help to model social networks.