HKS Students and Research Affiliates

Intelligence organizations often fail to anticipate strategic developments even when relevant information is available because signals remain fragmented, ambiguous, overwhelming, or filtered through entrenched assumptions. The September 11 attacks, Russia’s 2022 invasion of Ukraine, and the October 7, 2023 Hamas attack illustrate how, even within some of the world’s most capable intelligence organizations, available indicators do not reliably translate into effective strategic warning, with outcomes shaped by differences in collection, interpretation, dissemination, and uptake. This article examines these failures and introduces AI-INT, or Artificial Intelligence Intelligence, as a response. AI-INT is an integrated, actor-specific synthesis-and-simulation layer within all-source analysis. It assembles and fuses relevant multi-source intelligence to build and update actor models, then uses them to run simulations at scale under systematically varied assumptions and conditions. It builds on wargaming’s value in deepening understanding, challenging assumptions, exposing blind spots, and exploring alternative trajectories by combining fused intelligence with LLM- and ML-based methods to expand the scenario space. As a bounded architectural illustration, the article presents SESBot, a Behavioral-AI framework for simulating the decision-making of selected Semi-State Terrorist Organizations (SESTOs). SESBot combines LLM-based agents with machine-learning behavioral anchors, illustrating one implementation of intelligence-grounded actor simulation within AI-INT. AI-INT’s promise lies not in predicting the future with certainty or replacing human judgment, but in helping analysts and decision-makers test assumptions, compare alternative scenarios, identify analytic blind spots, surface overlooked trajectories, connect them to monitorable indicators, and communicate uncertainty rigorously.

Thailand’s attempts to use economic power to influence the outcome of its border conflict with Cambodia in 2025 largely failed, due to major changes in the Cambodian economy that have lessened its reliance on its richer neighbour. The conflict’s aftermath highlights how the growth and diversification of Southeast Asian economies increasingly precludes regional governments’ use of so-called ‘geoeconomic’ strategies against one another.

This article analyzes the multidimensional crisis, ideological, operational, and logistical, confronting Hezbollah following the dramatic events of 2023–2026. Several factors precipitated this unprecedented transformation. These include Operations "Arrows of the North" and "Lion's Roar," deep intelligence infiltration into the organization's ranks, and the loss of its historic leadership, primarily Hassan Nasrallah. Furthermore, Hezbollah faces the collapse of its Syrian strategic rear, a resource crisis in Iran, and a shifting posture from the Lebanese state. Paradoxically, continued IDF presence in the south and active fighting provide Hezbollah with a ready justification for its survival, even as negotiations threaten to permanently delegitimize its armed existence. The article traces Hezbollah's ideological evolution through its two foundational documents: the 1985 Open Letter, which focused on Islamic ideological identity, and the 2009 Political Document, which emphasized Lebanese national identity and political integration. Drawing on a textual analysis of the writings of the current Secretary-General, Sheikh Naim Qassem, the study assesses how a prospective "Third Open Letter" might appear. I argue that to justify its continued existence, the organization will likely partially retreat from its Lebanese national aspirations and reinforce a clandestine, sectarian-Shi'ite identity. Consequently, Hezbollah may replace its ambitions for decisive military victory with a theology of survival, framing the destruction as a "divine trial", and concentrate its efforts on liberating Lebanese soil from the IDF's presence and sabotaging peace negotiations between Israel and Lebanon. Hezbollah is likely to maintain formal allegiance to Tehran absent a dramatic ideological shift in Iran. Khamenei's death, however, is assessed to reduce the depth of the commitment to Iran.

Given the rapidly evolving nature of the conflict and the considerable uncertainty surrounding its outcome, the projections offered here should be understood as analytical assessments rather than definitive predictions. Multiple trajectories remain plausible, and this study makes no claim to forecast outcomes with certainty.

The 2025 border conflict between Cambodia and Thailand brought great disruptions to both countries: nearly a million migrant workers were displaced, oil and agricultural goods had to be rerouted, and border closures interrupted daily life and supply chains. The impact on Cambodia so far appears milder than might be expected for a small economy that depended on trade and labour flows with its larger neighbour. In the last 10 years, Cambodia pivoted towards trade and investment with China and Vietnam, with the United States as the leading export destination. In 2022, Thailand fell from being Cambodia’s second-largest to its third-largest source of imports. Cambodia had relied on trade with Thailand in several key product areas, especially fuel, vehicle parts, and agricultural processing. Industrial machinery and intermediate goods trade quickly rebounded, while fuel and agricultural trade pivoted to other regional suppliers. Other areas of bilateral trade appear to be in structural decline. The conflict worsened incomes for migrant workers and border area farmers and businesses. Cambodia’s economy has continued to grow, buttressed by foreign investment and manufacturing growth around Phnom Penh and in the southeastern provinces. The conflict’s limited economic effect on Cambodia may reflect: the versatility of trade for small open economies with sea access and multiple neighbours; the geopolitical benefits of investments from larger economies; and the limits to geoeconomic tools among regional states. Cambodia’s future growth depends on how it stabilises displaced workers’ finances, diversifies and upgrades its industrial base, and fosters new growth drivers in its western provinces.

The rapid expansion of artificial intelligence infrastructure, including data centers and the energy, land, water, and labor systems that support them, presents regional policymakers with trade-offs that are poorly captured by the prevailing “innovation versus regulation” frame. This article develops the AI Infrastructure Triad as a conceptual framework for analyzing three competing priorities in regional AI infrastructure governance: Progress, Sustainability, and Equity. We argue that regions are unlikely to maximize all three simultaneously under current technological, institutional, and resource conditions. Drawing on prior work on the economic, physical, and moral limits of AI development, a previously coded dataset of 10,068 public comments submitted to the 2025 U.S. AI Action Plan and illustrative regional cases, the article interprets stakeholder and regional positions as different ways of prioritizing the triad’s frontiers. The evidence is used illustratively rather than as a full causal test. The paper’s contribution is to clarify the trade-offs that infrastructure decisions often obscure, distinguish deliberate triad governance from default allocation by market power or regulatory inertia, and propose a Deliberate Triad Choice Framework for policymakers considering AI infrastructure decisions of significant scale.

This review situates Rewiring Democracy within debates on artificial intelligence, democratic governance, and institutional design. In contrast to work that emphasizes technological determinism or narrowly defined risk mitigation, the review engages Bruce Schneier and Nathan E. Sanders’ argument that AI is becoming embedded in democratic institutions and everyday civic practices, shaping how governments process information, allocate authority, deliver public services, and mediate civic participation.

The book is positioned alongside scholarship on surveillance capitalism, infrastructural power, and sociotechnical governance, while highlighting its distinctive contribution: a pragmatic framework for examining how AI may reconfigure legislatures, administrative agencies, courts, and civic life under conditions of democratic constraint. The review argues that the book is strongest when it treats AI as a governance challenge rather than merely a technical tool, but less complete when it moves from plausible use cases to durable institutional reform. It also assesses the book’s methodological orientation, noting both its strengths in conceptual synthesis and its limits in institutional specification, political economy, and democratic theory.

Event-level terrorism datasets are designed for incident-level analysis, whereas many research questions require data better suited to organizational trajectories and within-organization change over time. We introduce TOQA (Terrorist Organization Quarterly Activity), a reproducible organization-quarter panel dataset derived from the Global Terrorism Database (GTD) for 1970-2020. TOQA enables large-N analyses of escalation, dormancy, and organizational change, covering 2,868 organizations while preserving inactive periods. It addresses three recurring data-construction problems: fragmented organizational identities, misleading inactive periods caused by miscoded lifespans, and weak integration between terrorism and conflict data. Across these challenges, TOQA systematically handles 984 GTD perpetrator labels through alias harmonization and structured exclusion of non-organizational entities. TOQA also links organizations to 439 UCDP Actor IDs, incorporating one-sided violence measures and facilitating integration with civil war and other conflict-related datasets. The resulting panel improves researchers’ ability to trace organizational trajectories over time, compare behavior across contexts, and draw stronger causal inferences by making sequencing, lagged relationships, and within-organization change observable. A Random Forest classifier illustrates one use of TOQA by forecasting whether an organization will record any fatalities in the next quarter. On the same matched sample, the integrated GTD-UCDP model outperforms a GTD-only model, illustrating the advantages of integration.

Semi-State Terrorist Organizations (SESTOs) are armed groups that not only control territory but also govern civilian populations, blending militant violence with governance functions. Despite their rarity, SESTOs account for a disproportionate share of terrorist attacks and fatalities globally. Here we identify 24 SESTOs using explicit criteria applied to organizations in the Global Terrorism Database (1970–2020), constructing an organization-quarter panel to compare their violent activity against thousands of non-SESTOs. Fixed-effects regressions reveal that SESTOs sustain significantly higher attack frequencies and aggregate fatalities without systematically increasing per-attack lethality or targeting state actors disproportionately. Notably, SESTOs exhibit episodic, extreme bursts of violence. A Random Forest machine learning model trained solely on violent activity accurately distinguishes SESTOs from non-SESTOs, confirming their distinct operational profile. These findings suggest that governance capacity shapes SESTOs' sustained and intense violence, highlighting their outsized threat and the need for nuanced policy responses that consider their unique behavioral patterns.

This thesis investigates the role of Artificial Intelligence (AI) as a catalyst for the climate transition, analyzing how AI enables emissions reduction, resource optimization, and advanced climate-risk measurement across energy, mobility, agriculture, finance, and urban systems. With foundational models increasingly commoditized, competitive advantage shifts toward high-quality data, integration capabilities, and alignment with emerging ESG and disclosure standards. The study proposes a structured framework for evaluating and deploying Climate AI solutions, emphasizing measurable climate impact, policy-fit, and system-level scalability. Case studies illustrate how AI-enabled platforms—particularly in carbon accounting, smart grids, predictive industrial systems, and nature monitoring—can accelerate climate outcomes when supported by coordinated stakeholder action. The thesis concludes that AI is evolving into essential climate infrastructure, requiring investment strategies that merge venture-style innovation with long-horizon sustainability priorities.

This report applies behavioural science to analyse the persistent decline in routine childhood immunization in Mozambique. Using Ipsos household data, latent class analysis, and gender-dynamics mapping, it identifies socio-cultural, cognitive, and informational barriers that limit vaccine uptake. A review of 58 global interventions informs a set of behaviourally grounded recommendations—spanning community-led communication, mHealth reminders, social-norm messaging, and improved health-worker interaction—to address demand-side frictions. The report offers a scalable framework for strengthening routine immunization in low-resource settings.