Human–Machine Strategic Analysis · New Tools for Strategic Analysis
Power is usually measured by what states possess. Network analysis asks a different question: where do they sit, who depends on them, and what happens when their position is removed?
In March 2012, a handful of Iranian banks found themselves cut off from a system most people have never heard of. Every economy on Earth depends on it. There were no warships blockading a port and no embargo announced with fanfare. A cooperative headquartered outside Brussels simply voted, and Iran’s financial institutions could no longer send or receive the messages that authorize a cross-border transaction. The system was SWIFT, the Society for Worldwide Interbank Financial Telecommunication. Acting under EU regulation passed in response to US pressure over Iran’s nuclear program, SWIFT disconnected roughly thirty Iranian banks from its network in March 2012 (Farrell & Newman, 2019, pp. 68-69).
Iran’s oil reserves had not moved. Its military had not shrunk. Its diplomatic standing, such as it was, was not the trigger. What changed was Iran’s position in a network of financial messaging that has no real substitute. An EU official at the time called it “a very efficient measure… it can seriously cripple the banking sector in Iran” (Gladstone & Castle, 2012, as cited in Farrell & Newman, 2019, p. 69). Unwinding the SWIFT ban became a central bargaining chip in the nuclear negotiations that followed. A cooperative most readers have never heard of had more leverage over Iran’s economy in that moment than most of its conventional military assets combined.
Power as a Stockpile
Most power analysis still counts. It counts GDP. It counts military spending as a share of GDP. It counts divisions, carrier groups, and population. This is the implicit ledger behind most net-assessment exercises, most “who is winning” journalism, and most first-year international relations teaching. Power is treated as a stockpile, and stockpiles can be ranked.
Iran’s case does not fit that ledger. Nothing on Iran’s capability sheet moved. What moved was something the sheet does not measure at all: Iran’s location within a structure of financial dependence that Iran did not build, does not control, and cannot easily exit.
Power as Position
Hafner-Burton et al. (2009) gave this intuition a name and a structure. They defined power as a function of network position, and they identified three distinct versions of it (pp. 570-573). Access power belongs to well-embedded nodes that can draw on resources and shape information flow simply by being connected. Brokerage power belongs to nodes that bridge otherwise disconnected clusters. A colonial power, for example, gains leverage not from raw capability, but from being the only connective tissue between colonies that have no ties to each other. Exit-option power belongs to marginal, weakly embedded actors who can credibly threaten to leave a network altogether. Tellingly, the authors’ own illustration of that third category contrasts Iran’s comparatively open exit options with North Korea’s near-total dependence on China (pp. 572-573). This is the same state, and years before the same kind of network coercion this piece opens with.
The clearest statement of why position works this way comes from sociology, four decades earlier. Granovetter (1973) showed that strong ties cluster into dense, overlapping cliques. It is the weak ties that connect otherwise separate clusters. People find jobs, ideas, and opportunities disproportionately through those weak ties, because acquaintances sit in different circles and carry different information. A node that bridges two otherwise disconnected parts of any network, whether financial, social, or geopolitical, has influence that has nothing to do with its size. Its influence comes from the fact that other actors cannot get from A to B without going through it. Network theory has a precise name for this: betweenness centrality. It is the same property that makes a single airline hub going offline so disruptive (Victor & Khwaja, 2020). SWIFT’s leverage over Iran, in this vocabulary, is nothing more exotic than a very high betweenness score.
Farrell and Newman (2019) turned this into an explicit theory of coercion. Global economic networks, they argued, do not stay flat and reciprocal the way classical liberal theory expected. They reliably collapse into asymmetric hub-and-spoke structures. States that hold jurisdiction over the resulting hubs, and have the domestic institutions to act on it, gain two distinct tools. The first is the panopticon effect, an information advantage from data passing through the hub. The second is the chokepoint effect, the ability to cut others off entirely (pp. 54-56). This is a third category of power, separate from market size and from ordinary bilateral dependence. It is power that comes specifically from centrality.
Being well-connected, however, is not automatically the same as being powerful. Bonacich’s classic refinement of centrality, as summarized by Izquierdo and Hanneman (2006 explains being tied to well-connected, independent partners makes an actor central. Being tied to dependent partners with nowhere else to go is what makes an actor powerful. SWIFT is powerful not because it is large, but because so many dependent institutions have no alternative route around it. Conventional power-counting asks how much an actor has. Network analysis asks what would be cut off, and for whom, if that actor’s position were denied. These are different questions, and 2012 Iran is the proof.
The SWIFT Precedent
SWIFT’s centrality was not designed but it emerged. Before the 1970s, cross-border bank communication ran over slow, insecure telegram and telex networks. A handful of banks built SWIFT to replace them. It grew from a few hundred member firms in 1977 to over 11,000 institutions worldwide by 2016 (Farrell & Newman, 2019, pp. 58-60). Nobody set out to build a chokepoint but efficiency did.
That efficiency became leverage in two distinct stages. After the September 11 attacks, the US Treasury began compelling SWIFT transaction data to trace terrorist financing. This was the panopticon effect, and it operated for over a decade largely out of public view (Farrell & Newman, 2019, p. 67). The chokepoint effect arrived later, and it needed something the panopticon effect did not: coordinating institutional capacity across two jurisdictions. SWIFT sits on EU soil, so the US alone could not compel it to act. It took a US Senate threat combined with an EU Council regulation to force the 2012 disconnection (Farrell & Newman, 2019, pp. 67-68).
That combination of jurisdiction and institutional capacity turns out to be the general mechanism behind coercive network power, not just SWIFT’s particular case. Hafner-Burton and Montgomery (2012) tested the standard liberal-peace claim that shared trade-agreement membership reduces conflict. They found that the claim only holds when dependence between two states is roughly symmetric. Where one state depends on a trading partner far more than that partner depends on it, conflict risk rises rather than falls, and the effect is larger than the pacifying effect of membership itself. Real, named disputes bear this out among states that were simultaneously trade-agreement co-members, including Armenia and Azerbaijan, India and Pakistan, and the two Koreas. Shared membership only became leverage when it produces asymmetric dependence. Even then, using it requires matching institutional capacity, not just jurisdiction.
The 2018 coda sharpens the point. When the US withdrew from the Iran nuclear deal and moved to reimpose sanctions, French Finance Minister Bruno Le Maire objected that the US was not “the economic policeman of the planet” (as cited in Farrell & Newman, 2019, p. 42). Yet the EU’s fragmented regulatory structure meant it had no real counter-move. It could protest. It could not disconnect. The position that let the US and EU jointly weaponize SWIFT against Iran in 2012 is the same position that left the EU unable to stop the US from threatening to do it again in 2018, this time without EU agreement.
When Structure Out-Predicted Power
A second, independently validated case makes the same point more starkly, because it strips away every psychological and institutional wrinkle SWIFT carries. Axelrod and Bennett (1993) modelled alliance formation with almost nothing but position. In their landscape theory of aggregation, each actor compares itself to every other actor pairwise, never to the group as a whole, and switches sides incrementally whenever doing so reduces its own local tension with its surroundings. There is no negotiation and no strategic calculation. There are only short-sighted, locally rational moves that mechanically pull the whole system toward a stable configuration.
The authors applied this model to the seventeen major European powers before the Second World War, using only pre-war data on ethnic ties, religion, borders, regime type, and history of conflict. The model correctly predicted the wartime alignment of all but two states. A pure capability-counting baseline, which treated every state as equally distrustful of every other, matched none of them. Run unchanged on nine computer manufacturers competing to set a technology standard in 1988, the same formalism correctly predicted most of the resulting alliance structure from market position alone. Position and structure predicted real outcomes years in advance, across two unrelated domains. Raw power comparison did not.
The theory also explains why some alignments prove so hard to shift. A system can settle into more than one stable configuration. A small early event can lock it into a worse-than-necessary arrangement. This is the same “frozen accident” logic that explains why the world kept the QWERTY keyboard.
The Reversal
A third finding goes further than either case above, because it shows what happens when position is not just left out of an analysis, but actively misrepresented by the method used to study it. Standard statistical treatments of alliance data analyze each pair of states as though its alliance decision were independent of every other pair’s. This assumption sits awkwardly with the fact that alliance blocs visibly react to each other in real time. Historically, the Triple Entente was assembled step by step in direct response to the Triple Alliance forming first (Cranmer et al., 2012).
Cranmer et al. (2012) re-ran a well-known alliance dataset twice. The first run used the standard, position-blind method. The second used an approach that lets a state’s alliance choices depend explicitly on the choices already made around it, accounting for popularity effects and for states’ real preference for closing triangles by allying with the allies of their allies. Most familiar findings survived the switch. One of the field’s best-established claims did not. The claim that democracies are especially likely to ally with other democracies came out positive under the standard method. It came out negative once alliance interdependence was modeled properly, and the standard method’s estimate of the effect’s size was badly overstated in the process. A textbook finding, cited for decades, reversed sign entirely once network structure was actually accounted for. This is the sharpest available demonstration that ignoring position does not just add noise to an analysis. It can flip the conclusion outright.
Picking Sides: The Logic of Triangles
If alliance choices react to each other, one useful answer for how predates network science by decades. In the 1940s, the psychologist Fritz Heider proposed that people seek consistency in how they feel about the people and things around them. Your friend’s friend should be your friend. Your friend’s enemy should be your enemy. An inconsistent arrangement creates real tension that people are motivated to resolve.
Corbetta and Grant (2012) applied this logic to a common situation in international politics: a state watching two others already locked in conflict, deciding what to do. If the third state is friendly with both disputants, the theory predicts it resolves the tension the cheapest way, through neutral mediation. If it is already friendly with one and not the other, its position is already consistent, and it has every incentive to act on that consistency through partisan intervention on the side of its friend. The authors tested this against the 1962 Sino-Indian War, where outside interventions are a matter of public record. This simple rule correctly predicted most states’ choice of mediator or partisan role from nothing more than the sign of their prior relationships. The same pattern held up statistically across a much larger dataset of Cold War and post-Cold War disputes, though somewhat more noisily in the later period. One result was genuinely surprising: shared regime type between the third party and a disputant did not significantly predict which role a state took. This cuts against a substantial body of research that treats regime similarity as a strong predictor of alignment.
More Ties, More Conflict
A separate, system-wide question asks whether more alliance connections overall make the international system more or less conflict-prone. The intuitive liberal answer is fewer disputes. Maoz et al. (2003) tested this across nearly two centuries of data and found the opposite. Years with denser alliance networks saw significantly more militarized disputes, not fewer. A related measure cut the other way: a more polarized system, split into a smaller number of tightly knit blocs, saw fewer disputes than a more loosely fragmented one. This is the opposite of what intuition about polarization would suggest. The same data also extended the well-known finding that pairs of democracies rarely fight each other up to the level of the whole system. The more democracies embedded in a state’s own network of ties, the less conflict-prone that state was. Structure, again, was doing work that a simple count of ties or capabilities would miss entirely.
What a Rising Power Does With Its Position
The access-brokerage distinction introduced earlier has a further use beyond explaining a single coercive episode. It helps explain what a rising or dissatisfied power is likely to do next. Political scientist Lauren Goddard, as summarized by Menninga and Goldberg (2022), distinguished a state’s access, meaning how embedded it already is inside existing institutions, from its brokerage options, meaning whether it instead bridges networks that existing institutions do not fully connect. Access gives a state standing to push for reform from within. Brokerage gives it the ability to build alternatives outside the existing order. A state rich in access has every incentive to reform the system it sits inside. A state rich in brokerage options has more reason to route around it and build parallel arrangements instead. Network position, in other words, does not just grant leverage over a single adversary. It shapes whether a dissatisfied power tries to reform, exit, or overturn the order it is embedded in, independent of its raw size.
Related findings point the same way at smaller scales. Diplomatic recognition spreads through this kind of “friend of my friend” clustering rather than through states’ own attributes. International systems broken into several tightly knit but poorly connected blocs are measurably more conflict-prone than more thoroughly interconnected ones (Menninga & Goldberg, 2022).
Reading a Network: Three Questions for Analysts
An analyst trying to map who has power in a given domain should stop starting with capability tables. Three questions work better.
Where are the hubs? In finance it is SWIFT and dollar clearing. In energy it is chokepoints like the Strait of Hormuz. In data it is a small number of transcontinental cables and cloud providers. The same pattern recurs across very different domains (Farrell & Newman, 2019).
Who has jurisdiction, and do they have the institutions to use it? Jurisdiction without capacity is inert. This is the EU’s problem with SWIFT. Capacity without jurisdiction is moot. Mapping the two separately avoids assuming that hosting a node is the same as controlling it.
Is dependence symmetric or asymmetric, and is the actor central or merely well-connected? Symmetric dependence rarely produces conflict. Asymmetric dependence does. Bonacich’s distinction adds a second layer. An actor tied to strong, independent partners is central without necessarily being powerful. An actor whose partners have nowhere else to go is powerful precisely because of that dependence.
One add-on is worth remembering. The panopticon and chokepoint effects do not automatically travel together. The US has near-total panopticon capability over the internet, but it has largely declined to build matching chokepoint capability there, for ideological and institutional reasons (Farrell & Newman, 2019, pp. 70-74). Any hub-mapping exercise should ask about both effects separately.
The Limits
Five real cautions follow:
Network logic is not one thing. Trade networks form through a rich-get-richer dynamic. Alliance networks form mainly through similarity between partners, and essentially never the other way around (Maoz, 2012). Network power in the Farrell and Newman sense applies cleanly to hub-based systems like SWIFT. It does not automatically transfer to alliance-type networks, and this piece should not imply that one master logic explains both.
Ties are not always what they look like. Shared institutional membership is a weak, sometimes misleading proxy for a real, influential tie. More than one source in this literature raises this caution independently (Hafner-Burton et al., 2009; Kacziba, 2021).
The data itself has a blind spot exactly where hub-mapping needs to see clearly. Peripheral, weakly connected nodes are disproportionately the ones that go unrecorded, which mechanically understates a network’s true density and centrality. This is close to the worst-case failure mode for an analysis whose whole purpose is finding the best-connected nodes (Menninga & Goldberg, 2022; Victor & Khwaja, 2020).
Weaponizing a hub is not free, even for the state doing it. States targeted through network position build workarounds. Russia has explored alternative payment systems, and China accelerated domestic chip manufacturing after being cut off from US suppliers (Farrell & Newman, 2019, pp. 76-77). Network power is real, but using it too aggressively can erode the very structure that generated it.
Position correlating with an outcome is not proof that it caused it. A node’s central position and its apparent power can both be downstream of some third factor. States with strong institutions may simply be more likely to both occupy hub positions and wield influence, with neither one causing the other. Researchers applying formal sensitivity analysis to two similar-looking claims about how behavior spreads through social networks found that one claim held up against plausible confounders while a near-identical one did not (Victor & Khwaja, 2020). The same method validated one claim and undercut another that looked just as plausible. Nothing in this piece proves that SWIFT’s position caused its leverage rather than merely coinciding with the institutional capacity to use it. The case is compelling because the mechanism is well documented, not because correlation between position and power is self-evidently causal.
One further clarification belongs alongside these five cautions. Granovetter’s tradition of informational bridging through weak ties and Farrell and Newman’s tradition of coercive chokepoints are related but not identical. Both are about the advantages of a bridging or central position. But “influence flows through weak ties” and “states can coerce through hubs” are structurally similar, analytically separate claims.
Where This Leaves Us
Kacziba (2021) surveyed how political scientists have reacted to network methods generally and identified three postures. Some scholars treat network methods as a narrow, borrowed tool. Others treat them as a genuinely expanding methodology. A smaller group holds what Kacziba calls “network idealism,” the belief that network thinking can redefine the field’s grand theories outright. This piece sits in the cautious middle. Network analysis does not replace capability, institutions, or history as explanations for what states do. What it adds is a variable that conventional power-counting cannot see at all. In case after case above, that variable turns out to be doing real, measurable, sometimes decisive work.
Return, one last time, to 2018. There was the reimposition threat, Le Maire’s line about the economic policeman, and the EU quietly unable to do more than object. The position that gave the US and EU joint leverage over Iran’s economy in 2012 is the same position that left the EU unable to stop the US from threatening to do it again six years later, despite SWIFT sitting on EU soil the entire time. Iran’s oil was where it had always been. Its army was where it had always been. What had changed, in both directions, was never on the capability sheet at all. It was position in a network, and whoever controls that position controls what happens next.
References
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