Leveraging Artificial Intelligence to Prevent Armed Conflict through Predictive Trade Policies

Executive summary

Economic pressure usually arrives before the shooting starts. Sudden import cut-offs, commodity price spikes, and broad sanctions can hollow out livelihoods, drain a government's legitimacy, and hand hardliners a ready-made enemy, often months before a crisis turns violent. This paper argues that this gap between economic stress and armed conflict is measurable, and that it can be put to work. It proposes Economic Diplomacy 4.0: a framework that uses artificial intelligence to read trade, commodity, and financial signals, flag the places where economic stress is building toward conflict, and give decision-makers concrete, graduated options to act while there is still time.

The central claim is deliberately narrow and testable: because trade is measurable and touches the daily lives of millions, the economic stress that often precedes conflict can be detected early enough to act on, and building the institutions to do so is a high-return investment in peace. The architecture has four layers (data, analysis, policy response, and governance) and six instruments, anchored by a UN-hosted Predictive Peace Data Hub. Unlike earlier versions of this idea, the proposal here is grounded in early-warning systems that already work, in a documented case where a trade shock fed real violence, and in a real precedent for why sovereign states would agree to share sensitive data at all.

1. Introduction and rationale

Global interdependence is both a source of prosperity and a source of fragility. A single import restriction can destabilise a dependent economy; a commodity price spike can erode political trust overnight; a broad sanctions package can radicalise a population as easily as it pressures a regime. Traditional conflict prevention tends to respond after a crisis is already visible, after prices have spiked, after protests have started, after the first shots. By then the cheapest options are gone.

This paper proposes a different sequence: use data-driven insight to reveal economic stressors early, and convert that warning into lead time for constructive diplomacy. The aim is not to replace political judgment with an algorithm, but to equip states and international organisations with a tested set of tools that turn warning into prevention. The focus is on trade-related pressures for a simple reason: trade is measurable, it touches many people, and it is a lever governments commonly reach for in a crisis.

2. How economic tension becomes conflict

Economic stress does not cause conflict on its own, but it reliably shapes the conditions in which conflict becomes more likely. Four pathways recur:

• Sudden scarcity. Restrictions on food, fertiliser, or energy create immediate hardship and give states coercive incentives to secure supply.
• Income shocks. Job losses and revenue collapse weaken a government's legitimacy, and leaders under pressure at home sometimes redirect that pressure into aggressive foreign policy.
• Sanction feedback loops. Broad sanctions can cripple an economy while emboldening the elites who blame external enemies for the pain, hardening rather than softening a regime.
• Strategic dependence. When a critical good, advanced semiconductors, rare inputs, is concentrated in one or two producers, the incentive to secure it by coercion or force rises.

The last few years supply live examples: Europe's scramble after its post-2022 dependence on Russian energy was exposed, and the pandemic's demonstration of how quickly concentrated supply chains can seize up. Each pathway leaves an economic footprint, in shipping volumes, prices, payment flows, and public rhetoric, before it becomes a security event.

3. What the evidence shows: early warning already works

The premise behind this paper is not speculative. Governments, researchers, and humanitarian agencies already run early-warning systems that turn data into months of advance notice, the proof of concept exists.

• The Violence & Impacts Early-Warning System (VIEWS), run by Uppsala University and the Peace Research Institute Oslo, produces monthly probabilistic forecasts of state-based armed conflict from one to 36 months ahead, at both country and roughly 55-kilometre grid-cell resolution, and evaluates its predictions out-of-sample against the latest conflict data (VIEWS, 2024).
• The Armed Conflict Location & Event Data Project (ACLED) collects real-time, disaggregated data on political violence and protest in every country, dates, locations, actors, and fatalities, that UN agencies, researchers, and journalists use to track escalation as it builds (ACLED, 2024).
• FEWS NET, created by USAID in 1985 after famines in Africa, forecasts acute food insecurity six to twelve months in advance by combining climate, market, trade, crop, and nutrition data (FEWS NET, 2024).

These systems establish two things this proposal depends on: that meaningful lead time is achievable, and that economic and trade data are among the most useful early signals. What they do not yet do is close the loop between an economic warning and a coordinated diplomatic and trade response. That gap is precisely what Economic Diplomacy 4.0 is built to fill.

4. A worked case: when a trade shock became violence

The clearest recent illustration is the global food-price crisis of 2007 to 08 and its echo in 2010 to 11. The FAO Food Price Index climbed to 231 points in January 2011, its highest level since the agency began measuring food prices in 1990 (FAO, 2011). The World Bank estimated that rising food prices pushed roughly 44 million people into extreme poverty in the developing world between June 2010 and early 2011 (World Bank, 2011). Food riots broke out in dozens of countries, and the event that ignited Tunisia's uprising, a street vendor's self-immolation, was rooted in economic desperation.

Researchers at the New England Complex Systems Institute went further, identifying a specific threshold on the FAO Food Price Index above which protests became likely, and showing that both the 2008 riots and the 2010 to 11 unrest across North Africa and the Middle East coincided with peaks in global food prices (Lagi, Bertrand & Bar-Yam, 2011).

The causal story is genuinely contested, and honest design requires engaging that. One careful critique notes that Tunisia's own price controls kept local food prices relatively stable during the crisis, and argues that high international prices were at most one trigger among many, with the real risk concentrated in low-income, import-dependent states that lack the capacity to buffer a price shock (Stimson Center, 2014). That objection does not weaken the case for an early-warning system; it sharpens its design. The lesson is that a spike in global prices is not a fuse everywhere. It becomes dangerous in a specific combination, heavy import dependence, weak fiscal buffers, and rising prices arriving together, and it is exactly that pattern, invisible to a single headline number, that a well-built model can learn to flag.

5. Economic Diplomacy 4.0: the architecture

The framework has four integrated layers:

• Data layer. Aggregates trade flows, shipping, commodity prices, financial and ownership records under privacy-respecting agreements, drawing where possible on existing open sources like ACLED and FEWS NET rather than building from scratch.
• Analytical core. Combines statistical early-warning models, causal analysis, scenario simulation, and natural-language processing of official rhetoric to distinguish ordinary market noise from the specific stress patterns that precede escalation.
• Policy response layer. Converts a warning into graduated, concrete options, quiet diplomacy, diversification support, stabilisation finance, humanitarian corridors, or more carefully calibrated sanctions, rather than a single alarm.
• Governance layer. Sets the rules on privacy, auditability, and human oversight so that the tool cannot quietly become an instrument of surveillance or coercion.

6. Policy instruments

Six instruments give the framework concrete form: a UN-hosted Predictive Peace Data Hub that issues public low-risk and confidential high-sensitivity alerts; a Trade Diversification Protocol that identifies dangerous supply concentration and proposes realistic alternatives; a Sanctions Simulation Dashboard that pre-tests the economic and humanitarian impact of a sanctions package before it is imposed; Economic Stability Credits that provide temporary, targeted fiscal support to trade-disrupted countries; Public-Private Data Trusts through which firms contribute aggregated data for anonymised insight; and an Ethical Oversight Board that independently reviews model design and governance.

7. Why sovereign states would actually share the data

The hardest objection to this whole design is not technical, it is political. Why would any state hand its trade, stock, and financial data to a UN-hosted hub? Earlier versions of this proposal raised the sovereignty problem and then moved past it. It deserves a real answer, and there is a working precedent for one.

After the 2007 to 08 and 2010 to 11 food crises, which were made worse by panic export bans, including Russia's 2010 wheat-export ban, and by the fact that no one reliably knew how much grain actually existed, the G20 created the Agricultural Market Information System (AMIS) in September 2011, hosted by the FAO. Through AMIS, G20 members plus Spain and other major exporting and importing countries share data on the production, stocks, and trade of wheat, maize, rice, and soybeans, specifically to improve market transparency and coordinate policy in times of uncertainty (FAO, 2024; AMIS, 2011).

States joined AMIS not out of altruism but out of self-interest: opacity had made every panic worse, and shared information dampened the wild price swings that hurt exporters and importers alike. Economic Diplomacy 4.0's data hub is AMIS's logic extended from grain markets to the broader set of trade signals that precede conflict. The incentive is identical, a state contributes because the alternative, flying blind into a shock that everyone else is also mismanaging, is worse for it. AMIS also shows the realistic shape of participation: start with a coalition of the willing, keep the most sensitive data aggregated, and let the value of the shared picture pull others in.

8. Implementation and a falsifiable pilot

Implementation should proceed in phases: a small set of regional pilots; institutionalisation under a UN mandate with IMF, WTO, and regional-bank partners; operationalisation of the dashboards and credit facility; and, only if the evidence supports it, global scaling. Three pilot contexts fit the four pathways above, Black Sea energy, Indo-Pacific semiconductors, and West African food and fertiliser.

The West African food-and-fertiliser pilot is the natural place to start, because the underlying signals are already partly monitored by FEWS NET and ACLED, and because it is the clearest test of the argument in Section 4. Crucially, it should be judged against pre-registered, falsifiable success criteria set before the pilot begins and audited independently, so that it can genuinely fail:

• Lead time. The system must flag a district's rising stress at least three months before a measurable escalation in ACLED-recorded unrest, matching the kind of horizon VIEWS and FEWS NET already achieve.
• Accuracy against a baseline. Its out-of-sample forecasts must beat a simple benchmark (for example, a price-trend or last-value model), evaluated monthly the way VIEWS evaluates its own models, catching the majority of true escalation episodes while holding false alarms low enough that diplomats will actually act on them. The exact hit-rate and false-positive thresholds are fixed in advance.
• Diplomatic uptake. At least one documented case in which an alert produced a concrete preventive action, emergency procurement, tariff relief, or a humanitarian corridor, weeks before conditions deteriorated.

If the pilot misses these targets, it is judged a failure and redesigned, not quietly expanded. That falsifiability is the point: a peace-prediction tool that can never be shown to be wrong is not evidence, it is faith.

9. Risks, safeguards, and sovereignty

A system this powerful carries real risks, and each needs a specific safeguard. Data abuse and surveillance is contained by defaulting to aggregated, anonymised data, limiting raw access, and requiring independent authorisation for any detailed request. Model bias and false positives, the danger that a wrong alert becomes a pretext for coercion, is why every high-level alert requires human review, a documented rationale, and standing red-team exercises, and why the false-positive rate is a pre-registered pass/fail metric rather than an afterthought. Political manipulation is reduced by independent technical oversight and rotating leadership so that no single state captures the hub. And legal complexity is handled through voluntary agreements that help states build frameworks balancing cooperation with rights, the AMIS model of participation, not compulsion.

10. Financing and the cost-benefit case

Seed funding can blend member-state contributions with philanthropic capital, while development banks capitalise the Economic Stability Credits facility. A modest fee-for-service model can sustain the system over time, with essential alerts kept free for low-income countries, the states least able to pay and most exposed to trade shocks.

The economics strongly favour prevention. The joint UN-World Bank study Pathways for Peace concluded that a scaled-up system of preventive action could save between US$5 billion and nearly US$70 billion per year, and stated plainly that prevention is cost-effective (United Nations & World Bank, 2018). Against the human and fiscal toll of armed conflict, the cost of an early-warning hub is a rounding error.

11. Conclusion

Preventing armed conflict is one of the highest possible returns on public investment. Economic Diplomacy 4.0 is a practical way to move from hindsight to foresight, not by replacing human judgment, but by amplifying it with data that already exists and warning systems that already work. The technical building blocks are in reach: the forecasting methods of VIEWS, the real-time data of ACLED and FEWS NET, and the cooperative logic of AMIS. What remains is the political will to begin piloting and testing, honestly and falsifiably. That is where an idea becomes impact, and that choice is ours.

Sources

1. https://viewsforecasting.org/early-warning-system/, VIEWS forecasts state-based armed conflict 1 to 36 months ahead at country and grid-cell resolution.
2. https://acleddata.com/, ACLED provides real-time, disaggregated data on political violence and protest across all countries.
3. https://fews.net/about, FEWS NET (USAID, est. 1985) forecasts acute food insecurity six to twelve months in advance.
4. https://arxiv.org/abs/1108.2455, Lagi, Bertrand & Bar-Yam (2011) identify a food-price threshold above which protests become likely; the 2008 and 2011 unrest coincided with global food-price peaks.
5. https://www.fao.org/newsroom/detail/World-food-prices-reach-new-historic-peak/en, FAO Food Price Index reached 231 points in January 2011, the highest since records began in 1990.
6. https://www.prb.org/resource/rising-global-food-prices-threaten-to-increase-poverty/, World Bank estimate: ~44 million people pushed into extreme poverty by rising food prices since June 2010.
7. https://en.wikipedia.org/wiki/Agricultural_Market_Information_System, AMIS was established in September 2011 at the G20's request after the 2007 to 08 and 2010 food crises, to improve market transparency and policy coordination.
8. https://www.fao.org/newsroom/briefing-notes-detail/agricultural-market-information-system-(amis)/en, FAO: AMIS is an inter-agency platform of G20 members plus Spain and seven other major exporters and importers to enhance food-market transparency and policy response.
9. https://www.stimson.org/2014/were-high-international-food-prices-an-early-warning-of-the-arab-spring-probably-not/, Stimson Center (2014): a critique arguing international food prices were an unlikely trigger where local price controls held, with risk concentrated in low-income, import-dependent states.
10. https://www.un.org/peacebuilding/news/press-release-conflicts-surge-around-world-new-approaches-prevention-can-save-lives-and-be-cost, UN & World Bank, Pathways for Peace (2018): scaled-up prevention could save US$5 to 70 billion per year and is cost-effective.