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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.\n\nThe 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. \n\nThe 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.\n\nThe 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.\n\n**KEY TAKEAWAYS**\n\nModern 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.\n"},"intro":{"text":"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*. \n"},"anticipatoryImpact":{"text":"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.\n\n* Anticipated impact on who we are as humans\n* Anticipated impact on how we will all live together\n* Anticipated impact on the well-being of humankind and sustainable future of our planet"},"indicatorValues":[{"id":"66bbfdf2a228a415ee796514","value":"0.561","numericValue":0.561,"year":2024,"indicator":{"id":"65c55cf29e947c438698aa4d","name":"Anticipation Potential","title":null,"slug":"anticipation-potential","dataSetId":"ANTICIPATION_POTENTIAL","color":null}},{"id":"68a691420ac1330579fd794b","value":"0.6152","numericValue":0.6152,"year":2025,"indicator":{"id":"65c55cf29e947c438698aa4d","name":"Anticipation Potential","title":null,"slug":"anticipation-potential","dataSetId":"ANTICIPATION_POTENTIAL","color":null}}],"editions":[{"id":"66ab1bb636a8f2f336a557bf","name":"2024","slug":"2024","numericValue":2024},{"id":"684951c963371e51d83bdf31","name":"2025","slug":"2025","numericValue":2025}],"anticipatoryImpactImage":{"image":{"id":"image_gesda-platform/image-asset/psp-pl-4-25-4-5_image__PSP-PL4_25_4.5_gf3h6b","url":"https://res.cloudinary.com/shapeable/image/upload/v1760070707/gesda-platform/image-asset/psp-pl-4-25-4-5_image__PSP-PL4_25_4.5_gf3h6b.webp"}},"embeds":{"citations":[],"imageAssets":[]},"surveyObservations":{"text":"The Behavioural Science of Groups has a high overall Anticipation Potential score. It is an area at the convergence of disciplines that will accelerate advances and their impacts in the future. The scope for action is also high for all sub-topics considered. **Predicting the onset of armed conflict** and **Interactions with wider global trends** score highly because of higher impacts and because the fields are relatively new."},"color":{"id":"65c55cbc9e947c438698a317","name":"Gold","slug":"gold","value":"#F39323","darkValue":"#936523","veryDarkValue":"#31220c"},"banner":{"id":"68e9a092505810c8b5ba5261","name":"4.5.1 Data Driven Models of Collective Behaviour. 2026","description":{"text":""},"image":{"id":"image_gesda-platform/banner/4-5-1-data-driven-models-of-collective-behaviour-2026_image__4.5.1_Data_Driven_Models_of_Collective_Behaviour_nzklgo","url":"https://res.cloudinary.com/shapeable/image/upload/v1760141446/gesda-platform/banner/4-5-1-data-driven-models-of-collective-behaviour-2026_image__4.5.1_Data_Driven_Models_of_Collective_Behaviour_nzklgo.webp","thumbnails":{"mainBanner":{"url":"https://res.cloudinary.com/shapeable/image/upload/c_limit,w_1440/v1760141446/gesda-platform/banner/4-5-1-data-driven-models-of-collective-behaviour-2026_image__4.5.1_Data_Driven_Models_of_Collective_Behaviour_nzklgo.webp","url2x":"https://res.cloudinary.com/shapeable/image/upload/c_limit,w_2880/v1760141446/gesda-platform/banner/4-5-1-data-driven-models-of-collective-behaviour-2026_image__4.5.1_Data_Driven_Models_of_Collective_Behaviour_nzklgo.webp"}}}},"chartImage":null,"citations":[],"subTopics":[{"id":"66adc5ccfbdece682d0b6427","name":"Data-driven models of collective behaviour","path":"/sub-topics/data-driven-models-of-collective-behaviour","outlineNumber":"4.5.1","slug":"data-driven-models-of-collective-behaviour","__typename":"Platform_SubTopic","color":{"id":"65c55cbc9e947c438698a317","name":"Gold","value":"#F39323"},"topic":{"id":"66adc56cfbdece682d0b641f","slug":"behavioural-science-of-groups","path":"/topics/behavioural-science-of-groups"},"intro":{"text":"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](/citation/2025-04-4-5-1/) 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”."},"description":{"text":"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](/citation/2025-04-4-5-2/) 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](/citation/2025-04-4-5-3/)\n\nData 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."},"anticipationScores":{"text":"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: \n\n1. The *uncertainty* related to future science breakthroughs in the field\n2. The *transformative* *effect* anticipated breakthroughs may have on research and society\n3. The *scope for action* in the present in relation to anticipated breakthroughs. \n\nThis chart represents a summary of their responses to each of these elements, which when combined, provide the *Anticipation Potential* for the topic. See [methodology](/science-anticipation/methodology) for more information."},"anticipationScoresImage":{"id":"68e8a2f963d1c853e9788ce1","image":{"id":"image_gesda-platform/image-asset/4-5-1-sub-anti-2026_image__4.5.1_sub_anti_2026_kyx0eh","url":"https://res.cloudinary.com/shapeable/image/upload/v1760076522/gesda-platform/image-asset/4-5-1-sub-anti-2026_image__4.5.1_sub_anti_2026_kyx0eh.webp","url2x":null,"width":1200,"height":1200}},"horizons":[{"__typename":"Platform_Horizon","id":"66adc68bfbdece682d0b644a","name":"5-year horizon","slug":"5-year-horizon-5","intro":{"text":"Useful datasets expand"},"description":{"text":"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.\n"},"color":{"__typename":"Platform_Color","id":"65c55cbc9e947c438698a317","name":"Gold","slug":"gold","value":"#F39323"},"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89a","name":"5-year horizon","slug":"5-year-horizon","years":5,"title":"5-year","subtitle":"horizon"},"embeds":{"citations":[]}},{"__typename":"Platform_Horizon","id":"66adc6a3fbdece682d0b644f","name":"10-year horizon","slug":"10-year-horizon-5","intro":{"text":"The behavioural science research environment matures"},"description":{"text":"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.\n"},"color":{"__typename":"Platform_Color","id":"65c55cbc9e947c438698a317","name":"Gold","slug":"gold","value":"#F39323"},"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89b","name":"10-year horizon","slug":"10-year-horizon","years":10,"title":"10-year","subtitle":"horizon"},"embeds":{"citations":[]}},{"__typename":"Platform_Horizon","id":"66adc6cefbdece682d0b6454","name":"25-year horizon","slug":"25-year-horizon-4","intro":{"text":"Data reveals sources of collective phenomena"},"description":{"text":"\nWidely 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.\n"},"color":{"__typename":"Platform_Color","id":"65c55cbc9e947c438698a317","name":"Gold","slug":"gold","value":"#F39323"},"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89c","name":"25-year horizon","slug":"25-year-horizon","years":25,"title":"25-year","subtitle":"horizon"},"embeds":{"citations":[]}}],"indicatorValues":[{"id":"66bbfe3aa228a415ee79652d","value":"0.527","numericValue":0.527,"year":2024,"indicator":{"id":"65c55cf29e947c438698aa4d","name":"Anticipation Potential","title":null,"slug":"anticipation-potential","dataSetId":"ANTICIPATION_POTENTIAL","color":null}},{"id":"68ede8dbaf9e6d6d63271122","value":"0.590","numericValue":0.59,"year":2025,"indicator":{"id":"65c55cf29e947c438698aa4d","name":"Anticipation Potential","title":null,"slug":"anticipation-potential","dataSetId":"ANTICIPATION_POTENTIAL","color":null}}],"embeds":{"citations":[{"slug":"2025-04-4-5-1","url":"https://doi.org/10.1093/ajae/aau038","name":"Rising food prices, food price volatility and social unrest","authors":[{"name":"M.F. Bellemare"}],"authorShowsEtAl":null,"edition":null,"publication":"American Journal of Agricultural Economics","accessDate":null,"startPage":1,"volume":97,"footnoteNumber":1,"year":null},{"slug":"2025-04-4-5-2","url":null,"name":"Familiar Strangers: Repeated casual contact and intergroup relations in urban South Africa [Unpublished manuscript]","authors":[{"name":"P. Bollen"}],"authorShowsEtAl":null,"edition":null,"publication":"Department of Political Science Ohio State University","accessDate":null,"startPage":null,"volume":null,"footnoteNumber":2,"year":null},{"slug":"2025-04-4-5-3","url":"https://americaspoliticalpulse.com/elites/","name":"What are America’s elected officials talking about?","authors":[{"name":"Polarization Research Lab"}],"authorShowsEtAl":null,"edition":null,"publication":"America’s Political Pulse","accessDate":null,"startPage":null,"volume":null,"footnoteNumber":3,"year":null}],"imageAssets":[]}},{"id":"66adc664fbdece682d0b643f","name":"Predicting the onset of armed conflict","path":"/sub-topics/predicting-the-onset-of-armed-conflict","outlineNumber":"4.5.2","slug":"predicting-the-onset-of-armed-conflict","__typename":"Platform_SubTopic","color":{"id":"65c55cbc9e947c438698a317","name":"Gold","value":"#F39323"},"topic":{"id":"66adc56cfbdece682d0b641f","slug":"behavioural-science-of-groups","path":"/topics/behavioural-science-of-groups"},"intro":{"text":" The 21st century has provided ample evidence of the far-ranging effects of armed conflict. Beyond the obvious toll of immediate death and destruction, the interconnectedness of today’s world means there can be significant political, economic and social repercussions far from the fighting. As such, predicting conflicts — with the aim of preventing them — is a stated objective of organisations such as the United Nations and World Bank."},"description":{"text":"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](/citation/2025-04-4-5-4/)\n\nPurely 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.\n"},"anticipationScores":{"text":"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: \n\n1. The *uncertainty* related to future science breakthroughs in the field\n2. The *transformative* *effect* anticipated breakthroughs may have on research and society\n3. The *scope for action* in the present in relation to anticipated breakthroughs. \n\nThis chart represents a summary of their responses to each of these elements, which when combined, provide the *Anticipation Potential* for the topic. See [methodology](/science-anticipation/methodology) for more information."},"anticipationScoresImage":{"id":"68e8984763d1c853e9788c1b","image":{"id":"image_gesda-platform/image-asset/4-5-2-sub-anti-2026_image__4.5.2_sub_anti_2026_kwcqth","url":"https://res.cloudinary.com/shapeable/image/upload/v1760073783/gesda-platform/image-asset/4-5-2-sub-anti-2026_image__4.5.2_sub_anti_2026_kwcqth.webp","url2x":null,"width":1200,"height":1200}},"horizons":[{"__typename":"Platform_Horizon","id":"66adc615fbdece682d0b642c","name":"5-year horizon","slug":"5-year-horizon-4","intro":{"text":"Machine-learning models improve conflict risk-detection"},"description":{"text":"\nThe 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."},"color":{"__typename":"Platform_Color","id":"65c55cbc9e947c438698a317","name":"Gold","slug":"gold","value":"#F39323"},"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89a","name":"5-year horizon","slug":"5-year-horizon","years":5,"title":"5-year","subtitle":"horizon"},"embeds":{"citations":[]}},{"__typename":"Platform_Horizon","id":"66adc63cfbdece682d0b6433","name":"10-year horizon","slug":"10-year-horizon-4","intro":{"text":"“Explicable” machine learning facilitates better understanding of conflict prediction"},"description":{"text":"\nThe 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."},"color":{"__typename":"Platform_Color","id":"65c55cbc9e947c438698a317","name":"Gold","slug":"gold","value":"#F39323"},"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89b","name":"10-year horizon","slug":"10-year-horizon","years":10,"title":"10-year","subtitle":"horizon"},"embeds":{"citations":[]}},{"__typename":"Platform_Horizon","id":"66adc660fbdece682d0b643a","name":"25-year horizon","slug":"25-year-horizon-3","intro":{"text":"Conflict-prediction models adopted by global organisations"},"description":{"text":"Endorsement and adoption of these approaches by global multilateral organisations, and development of accompanying ethical and practical frameworks to ensure that predictions instil confidence rather than hastening conflict and that interventions are timed and designed to respect the autonomy of those affected."},"color":{"__typename":"Platform_Color","id":"65c55cbc9e947c438698a317","name":"Gold","slug":"gold","value":"#F39323"},"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89c","name":"25-year horizon","slug":"25-year-horizon","years":25,"title":"25-year","subtitle":"horizon"},"embeds":{"citations":[]}}],"indicatorValues":[{"id":"66bbfe64a228a415ee796538","value":"0.574","numericValue":0.574,"year":2024,"indicator":{"id":"65c55cf29e947c438698aa4d","name":"Anticipation Potential","title":null,"slug":"anticipation-potential","dataSetId":"ANTICIPATION_POTENTIAL","color":null}},{"id":"68ede91aaf9e6d6d63271131","value":"0.620","numericValue":0.62,"year":2025,"indicator":{"id":"65c55cf29e947c438698aa4d","name":"Anticipation Potential","title":null,"slug":"anticipation-potential","dataSetId":"ANTICIPATION_POTENTIAL","color":null}}],"embeds":{"citations":[{"slug":"2025-04-4-5-4","url":"https://doi.org/10.1093/jeea/jvac025","name":"The Hard Problem of Prediction for Conflict Prevention","authors":[{"name":"H. Mueller and C. Rauh"}],"authorShowsEtAl":null,"edition":null,"publication":"Journal of the European Economic Association","accessDate":null,"startPage":2440,"volume":20,"footnoteNumber":4,"year":null}],"imageAssets":[]}},{"id":"66adc737fbdece682d0b6474","name":"Optimisation of interventions","path":"/sub-topics/optimisation-of-interventions","outlineNumber":"4.5.3","slug":"optimisation-of-interventions","__typename":"Platform_SubTopic","color":{"id":"65c55cbc9e947c438698a317","name":"Gold","value":"#F39323"},"topic":{"id":"66adc56cfbdece682d0b641f","slug":"behavioural-science-of-groups","path":"/topics/behavioural-science-of-groups"},"intro":{"text":"Many instances of conflict stem from the deterioration of intergroup relations: one community or bloc begins to mistrust another, leading to reduced cooperation and overt frustration, which ultimately becomes outright antagonism. In response, social scientists have formulated interventions that aim to improve such relations by reducing bias, hostility or negative behaviours. This approach has had some success, as measured by improvements in key psychosocial variables such as trust and inclusivity. "},"description":{"text":"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](/citation/2025-04-4-5-5/) \n\nTo 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.\n"},"anticipationScores":{"text":"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: \n\n1. The *uncertainty* related to future science breakthroughs in the field\n2. The *transformative* *effect* anticipated breakthroughs may have on research and society\n3. The *scope for action* in the present in relation to anticipated breakthroughs. \n\nThis chart represents a summary of their responses to each of these elements, which when combined, provide the *Anticipation Potential* for the topic. See [methodology](/science-anticipation/methodology) for more information."},"anticipationScoresImage":{"id":"68e89fa263d1c853e9788c9f","image":{"id":"image_gesda-platform/image-asset/4-5-3-sub-anti-2026_image__4.5.3_sub_anti_2026_srko7m","url":"https://res.cloudinary.com/shapeable/image/upload/v1760075662/gesda-platform/image-asset/4-5-3-sub-anti-2026_image__4.5.3_sub_anti_2026_srko7m.webp","url2x":null,"width":1200,"height":1200}},"horizons":[{"__typename":"Platform_Horizon","id":"66adc70bfbdece682d0b6463","name":"5-year horizon","slug":"5-year-horizon-6","intro":{"text":"“Intervention science” matures"},"description":{"text":"Continued development of intervention science aims to create repeatable, context-sensitive approaches to intergroup conflict, in collaboration with local community members and with an emphasis on demonstrable real-world effectiveness, backed up by new sources of funding."},"color":{"__typename":"Platform_Color","id":"65c55cbc9e947c438698a317","name":"Gold","slug":"gold","value":"#F39323"},"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89a","name":"5-year horizon","slug":"5-year-horizon","years":5,"title":"5-year","subtitle":"horizon"},"embeds":{"citations":[]}},{"__typename":"Platform_Horizon","id":"66adc71efbdece682d0b6467","name":"10-year horizon","slug":"10-year-horizon-6","intro":{"text":"Careful deployment of interventions begins"},"description":{"text":"Interventions are deployed after careful assessment of particular actors (leaders who may exploit them, for example) and contexts (which may affect delivery and efficacy), while also recognising their mutual interactions to ensure net benefit. Researchers move even more toward collaborating with cultural institutions (museums, for example) and municipal leaders to inform the broader community about conflict narratives and opportunities for community investment."},"color":{"__typename":"Platform_Color","id":"65c55cbc9e947c438698a317","name":"Gold","slug":"gold","value":"#F39323"},"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89b","name":"10-year horizon","slug":"10-year-horizon","years":10,"title":"10-year","subtitle":"horizon"},"embeds":{"citations":[]}},{"__typename":"Platform_Horizon","id":"66adc735fbdece682d0b646f","name":"25-year horizon","slug":"25-year-horizon-5","intro":{"text":"Refined, customisable interventions improve intergroup relations"},"description":{"text":"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.\n"},"color":{"__typename":"Platform_Color","id":"65c55cbc9e947c438698a317","name":"Gold","slug":"gold","value":"#F39323"},"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89c","name":"25-year horizon","slug":"25-year-horizon","years":25,"title":"25-year","subtitle":"horizon"},"embeds":{"citations":[]}}],"indicatorValues":[{"id":"66bbfe7fa228a415ee796543","value":"0.556","numericValue":0.556,"year":2024,"indicator":{"id":"65c55cf29e947c438698aa4d","name":"Anticipation Potential","title":null,"slug":"anticipation-potential","dataSetId":"ANTICIPATION_POTENTIAL","color":null}},{"id":"68ede93faf9e6d6d63271140","value":"0.620","numericValue":0.62,"year":2025,"indicator":{"id":"65c55cf29e947c438698aa4d","name":"Anticipation Potential","title":null,"slug":"anticipation-potential","dataSetId":"ANTICIPATION_POTENTIAL","color":null}}],"embeds":{"citations":[{"slug":"2025-04-4-5-5","url":"https://doi.org/10.1038/s44159-024-00330-z","name":"Advancing research and practice of psychological intergroup interventions","authors":[{"name":"S. 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