Historically, researchers seeking to understand collective behaviour have been limited to comparatively simple models — for example, those based on correlations between the prices of basic foodstuffs and the likelihood of civic disorder.¹ But the increased availability of continuous data about movements, communication and interactions makes it possible to develop more refined models and test them against real data — a “bottom-up” approach focused on aggregated individual behaviours in addition to forces acting “top-down”.

Future Horizons:

×××

5-yearhorizon

Useful datasets expand

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.

10-yearhorizon

The behavioural-science research environment matures

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.

25-yearhorizon

Conflict prediction models adopted by global organisations

Endorsement and adoption of these approaches by global multilateral organisations, and the development of accompany ethical and practical frameworks to ensure predictions instil confidence rather than hastening conflict, and that interventions are timed and designed to respect the autonomy of those affected.

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 artificial intelligence 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.

Understanding intergroup dynamics - Anticipation Scores

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:

The uncertainty related to future science breakthroughs in the field
The transformative effect anticipated breakthroughs may have on research and society
The scope for action in the present in relation to anticipated breakthroughs.

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.