Social Capital Is a Design Choice
Why every AI agent you build is shaping the social fabric — and nobody is measuring it
Abstract: Trust between humans aggregates into social capital — the generalised trust, civic engagement, and institutional confidence that drive economic and institutional outcomes. My previous article [1] established that this aggregation is empirically detectable: trust behaviours quantified through the Castelfranchi-Falcone Socio-Cognitive Model correlate at 0.98 with regional GDP [2]. That finding was about humans trusting humans. This article asks what happens when one side of the trust interaction isn't human anymore. I argue that the Socio-Cognitive Model of Trust applies to human-AI interactions by its own internal logic — not by extension but by design — and that agent design choices are therefore social capital design choices, whether we recognise them as such or not. A companion paper [3] formalises this argument as a Markov Decision Process framework. Here, I lay out the reasoning in plain language.
The computational trust tradition has never cared whether the entities in a trust interaction are human. From Marsh’s 1994 formalisation [6] onward, trust was treated as a mathematical property of interactions between agents — any agents. Recommendation engines, reputation systems, and trust propagation networks all inherited this agnosticism. The math works regardless of what the trustee is.
My own research went in the opposite direction. Building on Castelfranchi and Falcone’s Socio-Cognitive Model of Trust [9], I studied trust in its strongest form — interpersonal, high-stakes, between cognitive agents who form genuine beliefs about each other’s intentions. The kind of trust where a parent decides whether to leave their child with someone they met online. Not relaxed definitions. Not reputation scores. The real thing.
That strict-sense research produced a finding that matters for what follows: trust behaviours quantified through the SCMT correlate at 0.98 with regional GDP [2]. Micro-level interpersonal trust aggregates into macro-level social capital. Not loosely. Almost perfectly.
That finding was about humans trusting humans. The social capital literature — from Putnam’s Italian civic traditions [4] to Coleman’s structural analysis [5] — describes a mechanism that has only ever operated in contexts where both parties are human. Nobody has examined what happens to that aggregation when one side of the trust interaction is an AI agent.
Two traditions that never talked to each other
The trust literature split decades ago into two camps that have largely ignored each other since.
The computational tradition begins with Marsh’s 1994 formalisation [6] — the first attempt to treat trust as a mathematical property of interactions between agents. Marsh gave us the trust continuum, the cooperation threshold, and the limit of forgivability. His framework was explicitly agent-agnostic. Whether the trustee was human was not assumed and didn’t matter. The tradition that followed — Sabater and Sierra’s reputation models [7], Golbeck’s trust propagation networks [8] — inherited this agnosticism. Technically sophisticated, practically useful, but psychologically thin. These models can tell you the probability that you should rely on someone. They can’t tell you what trust actually is.
The socio-cognitive tradition goes deeper. Castelfranchi and Falcone’s Socio-Cognitive Model of Trust (SCMT) [9] grounds trust in a specific configuration of beliefs within the trustor: that the other party has the opportunity to act, the competence to act effectively, and the willingness to act in the trustor’s interest. All three must be present. This model distinguishes trust from mere reliance — an ATM can be relied upon but not trusted, because it has no willingness. The SCMT gives trust the psychological depth that computational models lack, and it gives it computability — the belief components can be operationalised, measured, and compared across contexts.
The price of that depth was an assumption: willingness implies intentionality. The trustee must be a cognitive agent — an entity with goals, beliefs, and intentions. For most of the twentieth century, this was an unproblematic boundary condition. A programmed routine is not a cognitive agent. The line was clear.
It stopped being clear about two years ago.
The boundary that dissolved
Modern AI agents exhibit goal-directedness. They adjust strategy. They maintain coherent representations across extended contexts. Whether they are genuinely cognitive is a philosophical question that may never be resolved. But here is what matters, and what most discussions of AI trust miss entirely:
The SCMT was never a model of the trustee’s inner life. It was always a model of the trustor’s belief structure.
Castelfranchi and Falcone’s willingness condition was never a claim about what the trustee genuinely is — it was a description of what the trustor believes about the trustee. The framework was always about the believing subject, not the trusted object. An ATM was excluded not because anyone verified the absence of its intentions, but because humans reliably do not form willingness-beliefs about ATMs. They treat them as mechanisms.
The empirical question is therefore not whether AI agents have genuine intentions. It is whether humans interacting with them form willingness-beliefs. The evidence strongly suggests they do. The Capgemini Research Institute found that trust in autonomous AI agents dropped from 43% to 27% in a single year [10]. That is not a calibration problem. That is the socio-cognitive trust dynamic operating exactly as the theory predicts: willingness-beliefs formed, expectations were violated, trust collapsed. The framework is already working in this domain, whether we have noticed or not.
This dissolves the boundary between the two traditions. For the class of AI agents now being deployed, the distinction between computational and socio-cognitive trust is immaterial.
The implication nobody has drawn
In my previous article [1], I showed that the trust beliefs captured by the SCMT — quantified through the Castelfranchi-Falcone framework and calibrated against ground truth data on a high-stakes digital platform — correlate at 0.98 with regional GDP [2]. That finding established something important: micro-level trust interactions between individuals aggregate into macro-level social capital outcomes. Not loosely. Almost perfectly.
That was about humans trusting humans. But if the SCMT now applies to human-AI interactions — by the framework’s own logic, not by analogy — then human-AI trust interactions have entered the same aggregation mechanism.
The consequence is direct: agent design choices are social capital design choices.
The way we build agents — their transparency, their consistency, how they handle failure, the degree to which they replace or augment human-human interaction — is not just a product design question or an alignment question. It is a question about what kind of social fabric we are building or eroding, interaction by interaction, at scale.
The computational trust literature optimises for dyadic calibration — making individual interactions work well. The social capital literature has no framework for non-human participants. The connection between these two bodies of work has been hiding in plain sight, following directly from research that has existed for decades. Nobody has drawn it.
The displacement problem
The most troubling possibility is not that AI agents will be untrustworthy. It is that they will be perfectly trustworthy and still erode social capital.
Social capital forms through trust interactions between humans — the accumulated experience of vulnerability honoured, cooperation rewarded, defection punished. These interactions build generalised trust: the confidence that strangers will, on average, deal with you fairly. That generalised trust is what makes communities function. Putnam showed it drives everything from governmental effectiveness to economic productivity [4], and the equilibria are self-reinforcing: high trust produces conditions that generate more trust; low trust produces conditions that erode it further.
If AI agents increasingly handle our negotiations, our customer interactions, our professional relationships — if they substitute for human-human interaction rather than augmenting it — then the individual transactions may go perfectly well while the social capital generating mechanism quietly stalls. The trust happens. The social fabric does not thicken.
This is invisible at the individual level. You would never detect it in a user satisfaction survey. It would only become apparent at the community level, over time, in metrics that nobody is currently tracking in relation to AI deployment. And by the time it becomes apparent, Putnam’s path dependency suggests it may be very difficult to reverse. His Italian regional data shows equilibria persisting for centuries once established.
Why right now matters more than later
There is a timing argument that changes the urgency of everything above.
When Putnam studied social capital, he was always measuring mid-stream — looking at communities centuries into their equilibria, trying to infer the dynamics from cross-sectional snapshots. He could never observe the initial conditions because they happened generations before any measurement existed.
For human-agent social capital, we can. We are at t = 0. The baseline social capital stock before widespread agent deployment is measurable right now — the surveys exist, the economic indicators exist, and the methodology for inferring trust from platform interaction data exists [2]. We can establish initial conditions and track what happens as agent penetration increases.
This gives us something social capital research has never had: a prospective design opportunity rather than a retrospective analysis. We can choose which equilibrium we converge toward. But only if we start measuring now, and only if we start designing for it now. The window for proactive design exists precisely because the path has not yet been walked.
A framework for the right questions
In a companion paper [3], I formalise this argument as the ATDP framework — Agentic Trust Design for Positive Social Capital. The framework treats the problem as a Markov Decision Process where the state captures both the population-level distribution of trust beliefs and the aggregate social capital stock, the actions are agent design properties mapped onto Castelfranchi-Falcone belief components through a learned weight matrix, and the reward is social capital change.
The framework generates five testable predictions. The one I find most urgent is the displacement hypothesis: that high-substitution agent deployments will show a divergence between dyadic trust metrics (which may look excellent) and community-level social capital indicators (which may be declining). If this is right, we are optimising for the wrong thing.
The formal details, the null hypotheses, and the full MDP specification are in the paper. Here, three practical implications follow immediately:
If you are deploying AI agents at scale, you should be measuring social capital indicators alongside trust and safety metrics. Not just “do users trust this agent?” but “is this deployment affecting generalised trust and institutional confidence in the affected community?” These are different questions with potentially different answers.
The substitution degree — how much your agent replaces versus augments human-human interaction — should be a first-order design variable. Most agent design focuses on capability and safety. How much the agent displaces human connection is rarely an explicit criterion. It should be.
Regulators should be thinking about the cumulative social capital effect of agent deployment patterns, not just the safety and fairness of individual products. This requires population-level measurement, not product-level testing.
An unexpected consequence
One thing I did not anticipate when building this framework: it turns out to be a quantifiable test for the cognitive agency question.
If the framework’s predictions hold — if agent design properties affect social capital through the mechanisms described — then either the agents are cognitive in the sense the trust theory requires, or the entire population believes they are, which for social capital purposes is functionally equivalent. If the predictions do not hold, the agents have not crossed that threshold — and the degree of divergence provides a continuous measure of how far away they are.
It is a quantifiable, empirical test for a question that philosophy has been arguing about for decades. It was not a design goal. It fell out of the architecture — a structural consequence of connecting trust theory to social capital theory through a formal model.
The measurement falls out of the architecture.
Where this goes
This article plants a flag. The companion paper [3] provides the formal apparatus. Neither constitutes proof — they constitute a framework for asking questions that are not currently being asked, about consequences that are accumulating whether or not we have the tools to measure them.
The trust we build with machines is not a private transaction between a user and a product. It is a thread in the social fabric. We should know what pattern it is weaving before the cloth is complete.
References
[1] Prifti, Y. (2026). When Psychology Beats the Algorithm. Weighted Thoughts. https://weightedthoughts.substack.com/p/when-psychology-beats-the-algorithm
[2] De Meo, P., Prifti, Y., & Provetti, A. (2025). Trust Models Go to the Web: Learning How to Trust Strangers. ACM Transactions on the Web, 19(2). https://doi.org/10.1145/3715882
[3] Prifti, Y. (2026). Social Capital Is a Design Choice: A Markov Framework for AI Trust and Societal Outcomes. arXiv preprint. [link when available]
[4] Putnam, R.D. (1993). Making Democracy Work: Civic Traditions in Modern Italy. Princeton University Press.
[5] Coleman, J.S. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94, S95–S120.
[6] Marsh, S. (1994). Formalising Trust as a Computational Concept. PhD thesis, University of Stirling.
[7] Sabater, J. & Sierra, C. (2005). Review on computational trust and reputation models. Artificial Intelligence Review, 24(1), 33–60.
[8] Golbeck, J. (2005). Computing and Applying Trust in Web-Based Social Networks. PhD thesis, University of Maryland.
[9] Castelfranchi, C. & Falcone, R. (2010). Trust Theory: A Socio-Cognitive and Computational Model. Wiley.
[10] Capgemini Research Institute (2025). Rise of Agentic AI: How Trust is the Key to Human-AI Collaboration.
Ylli Prifti, Ph.D., writes about AI, cognition, and the structures that hold communities together on Weighted Thoughts.
If you’re working on trust systems, social capital measurement, or AI agent design — connect on LinkedIn or reach out.