India’s Economic Toolkit at Work
TAG’s geopolitics experts partnered with our data scientists to build a proprietary AI-powered simulation to model the interaction of key players in India. Five principal actors were each calibrated by regional and sectoral experts to reflect institutional policy preferences, political constraints, and realistic behavior under crisis conditions:
- Government of India: Representing the Prime Minister’s Office and the Union Council of Ministers that sets policy.
- Reserve Bank of India (RBI): India’s central bank that sets interest rates, manages foreign exchange reserves, and maintains monetary stability independently of the government.
- Parliament of India: India’s legislative body that controls budget approval and amplifies constituent concerns.
- Large industry: Major conglomerates, digital-native businesses and unicorns, IT services companies, and public-sector units (PSUs).
- Small and medium-sized businesses: The Indian economy’s largest employer by headcount, concentrated in manufacturing, food processing, and pharmaceuticals.
The simulation was run 50 times and modeled interactions over 180 days starting from June 11, with results assessed at the 90-day (mid-September) and 180-day (mid-December) points. Using the June 11 status quo as the reference point, each run was based on a distinct set of events in the Strait of Hormuz that produced more modest to more severe disruptions, enabling a distribution of outcomes rather than a single prediction.
Outcomes
The simulation found that India’s fiscal and energy institutions were able to navigate the crisis effectively in the first 90 days in all runs, but at the cost of compressing structural margins. The model also showed that the post-90-day period beginning in mid-September, particularly in runs where the severity of the disruption in the Strait of Hormuz continued to remain significant, was more challenging.
- Indian institutions had policy options to maneuver the crisis, but employing them entailed costs. Across all runs, the government deployed price caps and fuel subsidies, supplemented by diplomatic procurement support and compensation funds, and — in fewer cases — strategic petroleum reserve swaps to keep refineries operating at or near capacity and shield households from sharp fuel-price increases. While government approval remained relatively stable in 80 percent of runs, fiscal deficits consistently exceeded the government’s target of 4.8 percent of GDP for FY 2026-27, ending between 5.0 and 5.3 percent of GDP by mid-December, depending on the severity of the disruption.
- In high-severity runs of the simulation, India’s agriculture sector faced a multi-vector shock. Across all runs, India’s below-expected monsoon reduced yields, pushing food prices higher by September and October. In runs where Hormuz disruptions spiked and remained elevated, constrained LPG supply limited household access to cooking fuel. Fertilizer shortages compounded the monsoon-driven agricultural pressure. In 34 runs, food CPI breached 8 percent in the September-October window and remained elevated, though broadly stable, through mid-December. In scenarios where Hormuz disruption eased, LPG supply recovered toward pre-crisis levels and fertilizer availability was largely maintained.
India’s below-expected monsoon reduced yields, pushing food prices higher by September and October.
- The Reserve Bank of India deftly navigated a balancing act to keep inflation in check without sacrificing growth. Across a majority of runs, the RBI deployed foreign exchange market intervention as a tool for tightening financial conditions. It also welcomed moves by the Indian government to attract USD inflows from the Indian diaspora. However, close to 40 percent of runs saw the RBI increase the repurchase rate by 25-50 basis points, with the simulated agent viewing food price increase by the October 2026 Monetary Policy Committee meeting as structural rather than transient.
- The impact of an extended disruption hit small and large companies in India’s pharmaceutical industry differently. The production of advanced pharmaceutical ingredients (APIs) contracted in 37 of 50 runs due to supply chain disruptions for precursors needed to manufacture paracetamol, metformin, and ciprofloxacin. The disruptions disproportionately affected small and medium-sized businesses, which experienced a set of compounding stressors: working capital compression from higher input costs, tighter credit conditions reflecting the RBI’s tightening posture, and logistics disruptions on cash cycles as revenue uncertainty rose. By contrast, across all runs, large pharmaceutical manufacturers showed they had the resources to largely absorb the shock to pharmaceutical precursors derived from petrochemicals.
- While India successfully diversified energy sourcing in most runs, domestic oil refinery utilization fell substantially when disruptions were extended and particularly severe. Across runs, New Delhi increased engagement with non-Gulf crude and gas suppliers from the United States and Venezuela to Russia, Latin America, and Africa. While some of these partnerships were activated as emergency procurement measures in the more severe runs, the simulation also saw India deepen bilateral energy cooperation for long-term supply of commodities from key supplier governments. In particularly severe circumstances, India could not fully compensate through alternative sourcing arrangements and domestic refining dropped. However, in most runs, New Delhi’s engagements kept domestic production relatively close to baseline.
- The Strategic Petroleum Reserve was an emergency stabilizer, not a first-line crisis management tool. Across the simulations, actors showed conditional support for using the Strategic Petroleum Reserve (SPR): they were willing to release reserves under acute pressure but remained cautious about deploying it as a primary response tool in less severe disruptions. The consistent takeaway is that the SPR was treated as a last-resort buffer rather than a routine instrument for managing market volatility.
Simulation Insight: How Actors Navigated Key Tensions
- Government of India: New Delhi’s core policy options remained consistent in every run: whether or not to renew the retail fuel price freeze as oil prices rose, maintain the fertilizer subsidy at or near baseline, deploy the Essential Commodities Act in order to ration gas distribution, and expand its flagship food security welfare scheme (PMGKAY) as the primary social support instrument in response to food price pressure. The variation across runs was not in whether these instruments were used, but in which conditions they were deployed and the margins that remained afterward. The discipline of the institutional response was itself a source of political stability: visible consumer protection held the government’s political position steady even as the underlying fiscal position tightened
- GOI-RBI Relations: The scenario captured the push-pull between the GOI’s preference for an expansionary fiscal policy to cushion the impact of rising costs on Indian consumers and households, and the RBI’s efforts to keep inflation under control and the deficit in check. This dynamic was the heart of the RBI’s decision to pursue a range of policy steps, such as foreign exchange intervention and support for measures to raise U.S. dollars, including from the diaspora, before eventually raising rates by 25-50 bps.
- Parliament: Parliament’s role across the primary period was primarily one of constituency pressure and information amplification. Throughout all 50 runs, Parliament remained attuned to ground-level cluster data and inputs from small and medium-sized businesses in their parliamentary constituencies, using this information to advocate for greater support for rural and small trader communities.
“In-Game” Insights
- Small manufacturers engaged their local MPs to add political pressure to the Finance Ministry for additional support, framing the impact of higher input cost as a jobs issue in their constituencies.
About the Simulation
The Asia Group’s machine learning engineers and data scientists, in partnership with in-house regional experts, built a proprietary agent-based simulation of geopolitical decision making to chart out possible scenarios for how an extended closure of the Strait of Hormuz would affect Asian markets.
In the simulation, each actor (a state, agency, leader, company, constituency) is an autonomous AI agent that reasons over its own interests, forms beliefs, negotiates, and acts, while a central adjudicator (Control Center) governs the world and resolves each turn. Agent profiles are shaped by TAG’s country experts while behavior is constrained and calibrated by quantitative models, and every outcome is resolved probabilistically. Because the engine samples many parallel futures rather than one, it yields distribution over plausible outcomes instead of a single guess — letting analysts stress test decisions and surface low-probability escalation paths.