Political scientists, sociologists and computer scientists have begun to create systems in which autonomous agents have to find ways to cooperate by distinguishing trustworthy from untrustworthy agents. This has been applied to a wide range of problems, ranging from information-routing algorithms to online search rankings to recommendation algorithms. But there is broader sense in which trust and cooperation studies are useful — in modelling the way people behave in the groups that make up societies.¹⁶

Future Horizons:

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5-yearhorizon

Data veracity becomes a global research issue

The increased importance of data-gathering and analysis places a greater focus on data sources and their veracity. This leads to increased research in data-verification research. Managing trust and reputation are already battlegrounds for some actors.

10-yearhorizon

Stakeholders battle over reputation and trust

Reputation-building and trust further become key factors for stakeholders in a wide range of data-gathering disciplines ranging from news organisations to scientific institutions to national and multinational organisations. AI plays an increasing role in these processes.

25-yearhorizon

AI oversees data veracity

Machine vision and artificial intelligence become important arbitrators of trust in data, news and images. However, a cat-and-mouse game continues between malicious actors and those attempting to shut them down.

In any society, business or network, the ability to evaluate and then trust partners is a crucial component of co-operation.¹⁷ Applying trust modelling to the networks of actors at work in the diplomatic landscape has the potential to better model potential outcomes of discussions, votes and negotiations.

This work comes at a time when the role of trust in broader society has been thrown into sharp focus by the phenomenon of fake news, manipulated images and deepfake videos. The diplomatic landscape is powerfully shaped by the information that flows through it, and false and misleading information has huge disruptive potential.

A key emerging issue is the role of artificial intelligence and how it will be used to inform cooperative decision-making. At the heart of this question is whether AI systems will become better at interpreting the complex data fed into them or worse as AI-generated data distorts their view of the world. The possibility that AI systems could create a kind of artificial truth will be an important issue for the field.

Trust and Co-operation Modelling - 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.