Equipped with the tools of modern research, it is becoming possible to identify, monitor and predict how individuals cohere into groups, and how those groups behave and interact with each other — to assess collective emotion as well as collective action.
Efforts to understand conflict and cooperation have long been dominated by notions of history and geography. Attempts to avert or encourage conflict have been couched in terms of the diplomatic crafts of discussion and negotiation, staged under the aegis of (supposedly) neutral and multilateral organisations. Such efforts have had notable successes in the post-war era, but also notable failures. Great-power conflict has been avoided, but there have been numerous instances in which the established order has collapsed unexpectedly and abruptly.
The traditional tools of politics and diplomacy have proven increasingly impotent in the face of such challenges, particularly as the environment has become ever more complicated by technology, migration, climate change, polarisation and partisanship, rises in both secularism and religiosity, and geopolitical realignments. However, new tools have also emerged from the fast-developing science of group behaviour, which aims to understand the deep-seated reasons behind the formation and activity of distinct groups.
The availability of granular data is particularly significant in this respect. For example, mobile-phone data allows researchers to monitor individuals’ movements, interactions and information diets; this data can then be cross-referenced with their social and political activity, and aggregated into groups of any relevant scale or scope: nations, societies or communities, demographic segments and ethnic groupings, and so on.
The point of this activity is to understand how groups interact and particularly how they might come into conflict. Today’s efforts are more often geared towards resolution — trying to stop conflict once it has already begun — rather than prevention. New methods of monitoring both top-down forces (such as messaging from elites or economic trends) and bottom-up trends (prevailing social attitudes) are changing this, potentially allowing conflict to be anticipated. This should in turn allow interventions to be made in a more optimised fashion, suitable to the particular context and circumstances of an emerging conflict, rather than applied generically. This is a young field, however, and there is still much systemisation, experimentation and validation to be done.
KEY TAKEAWAYS
Modern research tools, especially computational resources, are making it possible to analyse the various aspects of group behaviour. This holds promise for enhancing cooperation and limiting conflict, as well as understanding the basic dynamics of how groups form and behave. Particularly useful are Data-driven models of collective behaviour, which can achieve unprecedented insights into the hidden drivers of observed behaviours. One of the most sought-after applications is in Predicting the onset of armed conflict. This will, in tandem with the Optimisation of interventions, give governments and global organisations the tools to mitigate the harm that intergroup conflicts bring to individuals and societies. Optimising all these actions depends on their Interaction with wider global trends such as climate change and access to burgeoning digital communication technologies, which affect the way group behaviours manifest and evolve.
Three fundamental questions guide GESDA’s mission and drive its work: Who are we, as humans? How can we all live together? How can we ensure the well-being of humankind and the sustainable future of our planet? We asked researchers from the field to anticipate what impact future breakthroughs could have on each of these dimensions. This wheel summarises their opinions when considering each of these questions, with a higher score indicating high anticipated impact, and vice versa.
The datasets available for analysis continue to expand — including increases in resolution (more precise records) and types of data (such as biometric data). This will require cooperation between data collectors and researchers, along with codes of conduct to ensure appropriate ethical and commercial oversight.
Integrated, cross-disciplinary teams become the norm in behavioural science, incentivised by a new wave of journals, funding opportunities and research organisations. Specialised analytical tools that draw on existing data competencies are developed but reflect the particular concerns and idiosyncrasies of social science.
Widely accessible, extremely granular data becomes routinely available for analysis. This is used to understand how collective behavioural phenomena result from cascading collective dynamics at the individual, group and societal levels, and how these relate to the social, economic, ecological and political environment.
For example, analysis of 1.2 billion mobile-phone pings in South Africa reveals that contact with a larger number of immigrants predicts more xenophobic attitudes and voting patterns. However, this effect is less pronounced if there are repeated casual interactions with the same members of the out-group.2 Another “top-down” example is the use of AI to categorise the way political leaders use language, thus revealing whether voters are hearing constructive debate or personal attacks.3
Data is not a panacea, however: there can be significant communities who are poorly represented in available data — for example, those who are not digitally connected — or hard to identify, such as those who are covertly resistant to autocratic regimes. And all the data in the world is of little use without the appropriate techniques for analysing it. These analytic approaches do not necessarily exist within the traditional social sciences and will need to be adapted and further developed in collaboration with specialists from other disciplines, such as physics and evolutionary biology.
The Anticipation Potential of a research field is determined by the capacity for impactful action in the present, considering possible future transformative breakthroughs in a field over a 25-year outlook. A field with a high Anticipation Potential, therefore, combines the potential range of future transformative possibilities engendered by a research area with a wide field of opportunities for action in the present. We asked researchers in the field to anticipate:
This chart represents a summary of their responses to each of these elements, which when combined, provide the Anticipation Potential for the topic. See methodology for more information.
The continued development of machine-learning models and other data-driven systems helps to improve detection of increased risk of conflict. The models are validated against historical and current experience to prove that they produce accurate predictions of the effects of conflicts and any interventions made to prevent them.
The development of explicable machine-learning systems allows their predictions to be better understood. Integration with theoretical models that capture top-down forces (such as communications from political elites) helps to refine predictions and shape interventions. The first real-world interventions (partially) based on such predictions occur.
This is not simple. It is easy to identify places whose social order is fragile or which are geopolitically exposed, but much more difficult to determine if and when this will tip into conflict, especially in countries with a long previous history of peace. Attempts are now being made to do this using machine learning, which recognises patterns in data to predict likely outcomes. However, this requires massive amounts of data. One such effort, for example, uses systems trained on millions of news articles dating back to the 1980s.4
Purely data-driven approaches, however, may or may not map onto prevailing theories of conflict escalation or outbreak. Nor do purely data-driven predictions offer any guidance as to the potential form of any intervention. Finally, there is also the inherent problem that any publicly disclosed prediction can itself influence the course of events by affecting public sentiment or government decision-making, or by making transparent the thresholds at which intervention becomes likely. Additionally, the use of large datasets in conflict areas raises potentially thorny questions related to surveillance and the potential misuse of data by combatants.
The Anticipation Potential of a research field is determined by the capacity for impactful action in the present, considering possible future transformative breakthroughs in a field over a 25-year outlook. A field with a high Anticipation Potential, therefore, combines the potential range of future transformative possibilities engendered by a research area with a wide field of opportunities for action in the present. We asked researchers in the field to anticipate:
This chart represents a summary of their responses to each of these elements, which when combined, provide the Anticipation Potential for the topic. See methodology for more information.
A full taxonomy of highly refined interventions can be deployed to improve intergroup relations, customised to particular circumstances and with full understanding of the appropriate timing and targeting for maximum effect.
However, this has also highlighted the need to optimise interventions for particular contexts. Interventions may be categorised by what they hope to achieve, how they hope to achieve it and how they can be delivered, but this needs to be supplemented with greater consideration of precisely what attitude or behaviour they are targeting, who they are trying to reach and how effectively they can be delivered within a particular operating environment. They also need to account for the level at which they operate — of individuals (for example, education), groups (exchange programmes) or societies (anti-discrimination laws).5
To achieve this, there will need to be greater structure and cross-disciplinary collaboration in the design of interventions. Perhaps most importantly, any research efforts will need to be developed and delivered in collaboration with local community leaders and citizens. Interventions at one level can have effects at different levels (anti-bigotry museum exhibits can encourage social mixing, for example) and over different timescales. They can also interfere with each other. That means they need to be carefully implemented in a way that allows them to be compared on a more systematic basis. Evaluation will need to be comprehensive and holistic (with great sensitivity to the possibility of unintended consequences), and geared towards practical solutions rather than improvements in some relatively abstract social variables.
The Anticipation Potential of a research field is determined by the capacity for impactful action in the present, considering possible future transformative breakthroughs in a field over a 25-year outlook. A field with a high Anticipation Potential, therefore, combines the potential range of future transformative possibilities engendered by a research area with a wide field of opportunities for action in the present. We asked researchers in the field to anticipate:
This chart represents a summary of their responses to each of these elements, which when combined, provide the Anticipation Potential for the topic. See methodology for more information.
Several megatrends are changing how intergroup conflict plays out. The changing climate, for instance, alters the viability of some lifestyles while opening up new ones — pastoralism versus mining in the Arctic, for example.6 The superpower face-off of the late 20th century has given way to a new, more multipolar environment in which nations may simultaneously collaborate and compete, or enter into new alliances and alignments. These factors are causing significant changes in global mobility and thus leading to novel intergroup interactions.
Combined with greater interconnection, this allows people to find and build common cause with others with whom they might previously have shared little affinity or even mutual awareness. This creates new groups which may span geographies — for example, climate activists engaging in stochastic disruption, or the formation of new political blocs. Conversely, it can lead to competition for resources, whether real or perceived, and friction over cultural norms. These consequences might equally arise from forced migration away from poorer countries as from lifestyle tourism into them. State actors may also exploit these trends, for example through online propaganda or proxy conflicts.
Overlaid on all these factors are the effects of digital communication, particularly those mediated by opaque algorithms. This can lead to distorted perceptions about both social opportunities and threats, including the motivations and intentions of others. Across contexts and cultures, overly negative “meta-perceptions” make people believe the worst of the other side and encourage support for hostile actions against them. Dramatic increases in polarisation suggest this is an increasing problem. Corrective information can de-escalate hostility, however, and engender a positive shift in attitude.
The Anticipation Potential of a research field is determined by the capacity for impactful action in the present, considering possible future transformative breakthroughs in a field over a 25-year outlook. A field with a high Anticipation Potential, therefore, combines the potential range of future transformative possibilities engendered by a research area with a wide field of opportunities for action in the present. We asked researchers in the field to anticipate:
This chart represents a summary of their responses to each of these elements, which when combined, provide the Anticipation Potential for the topic. See methodology for more information.
