Inform: Experiential Futures for Policy Design

• Inform is an approach that leverages participatory, tangible experiential futures to inform policy design by materializing diverse sociotechnical scenarios.
• It integrates HCI field trials with foresight methods to generate reflexive, inclusive evidence that addresses rapid technological innovation and policy lags.
• The method employs iterative prototyping and in-situ interventions—exemplified by smart garment trials—to reveal real-world risks and inform responsible regulatory strategies.

Searching arXiv for recent work related to experiential futures, policy design, and HCI-policy methods. Experiential futures in-the-wild are participatory, tangible, and interactive renderings of possible futures deployed in everyday settings so that policy design can be informed by lived, situated encounters with sociotechnical worlds before those worlds are locked in. Rather than relying on expert textual scenarios alone, this approach materializes pluralistic future visions through artifacts, performances, and interactions, making routines, values, assumptions, and unintended consequences legible to policy. In the formulation developed in “Experiential Futures In-the-wild to Inform Policy Design,” the approach is positioned as a response to the policy-novelty gap and as a mode of Responsible Research and Innovation (RRI) that strengthens anticipation, reflection, inclusion or deliberation, and responsiveness in policy processes (Sanchez et al., 2023).

1. Conceptual scope and policy rationale

The central problem addressed by experiential futures in-the-wild is the policy-novelty gap: technological innovation cycles move quickly, while policy development is slower, increasing uncertainty and forcing regulation to respond to unintended consequences after the fact. The proposed remedy is not simply better forecasting, but the creation of evaluation environments in which people can encounter plausible sociotechnical futures as part of ordinary life. This is meant to generate evidence before institutional and infrastructural lock-in occurs (Sanchez et al., 2023).

Within this framing, three terms are foundational. “Experiential futures” are participatory, tangible, and interactive renderings of possible futures that create real memories of virtual events. “In-the-wild interventions” are HCI field trials and design interventions conducted in ordinary contexts rather than laboratory settings, explicitly embracing the “messiness” of everyday life. “Ethnographic Experiential Futures” (EXF) is an orienting framework that renders futures legible through social and material forms and multiplies scenarios so that a single preferred future does not dominate the inquiry. The approach therefore treats future-making as a present-day design activity whose outputs can be interrogated politically, ethically, and institutionally, rather than as a purely speculative exercise (Sanchez et al., 2023).

The emphasis on pluralism is not incidental. The approach explicitly asks which futures are constructed, who benefits from them, and how findings from one intervention are interpreted toward other futures. This makes the method simultaneously anticipatory and reflexive: it seeks evidence about possible consequences while also examining the politics of representation embedded in the very act of constructing futures.

2. HCI, Responsible Research and Innovation, and the policy interface

The article’s argument for HCI engagement with policy design rests on the claim that HCI already materializes visions of the future in the present through prototypes, field trials, and design interventions. Because these practices foreground everyday experience, plural publics, and value-laden participation, they align closely with the governance logic of RRI. HCI is also presented as unusually sensitive to social, economic, legal, environmental, and political drivers of adoption, which makes it suited to policy contexts where purely technical analysis is insufficient (Sanchez et al., 2023).

The RRI dimensions used as the main policy frame are concise but operationally important.

RRI dimension	Policy role in experiential futures
Anticipation	Surface implications and futures
Reflection	Examine purposes and values
Inclusion/deliberation	Engage plural stakeholders
Responsiveness	Adjust goals and actions

These dimensions are not treated as abstract ethics vocabulary. They are used as a way to interpret intervention findings and to organize evaluation. Anticipation concerns the consequences and uncertainties surfaced across sociotechnical domains. Reflection concerns moments in which participants reconsider assumptions, goals, or purposes. Inclusion or deliberation concerns whose perspectives are represented and how broadly stakeholder participation is distributed. Responsiveness concerns whether the inquiry produces actionable adjustments to safeguards, standards, or policy trajectories.

This positioning also implies a distinctive role for HCI in governance. Iterative prototyping and in-the-wild deployment create agile, responsive evaluation environments in which weak signals, stakeholder reactions, and emerging harms can be examined faster than conventional policy cycles usually permit. A plausible implication is that HCI here functions less as downstream usability evaluation and more as a pre-regulatory instrument for eliciting socio-ethical evidence.

3. Methodological process for materializing plural futures

The proposed process combines foresight and HCI methods into a staged workflow that begins with futures exploration and ends with policy interpretation. The steps are presented as iterative rather than strictly linear.

1. Scan and multiply futures: participatory methods are used to map STEEPLE drivers, weak signals, and critical uncertainties, and to multiply scenario variants so that policy does not overfit to a single imagined future.
2. Materialize and situate: scenarios are translated into tangible, interactive artifacts and enactments embedded in homes, neighborhoods, or workplaces.
3. Elicit and reflect: participants encounter the future through routines and interactions, then narrate values, fears, assumptions, and social dynamics.
4. Interpret and inform: findings are connected to RRI dimensions and policy-relevant questions, and responsive policy options are co-produced with stakeholders (Sanchez et al., 2023).

Several concrete methods populate this pipeline. Future Ripples is a participatory foresight activity derived from the Futures Wheel and designed as a light-weight workshop format for mapping cascading consequences across STEEPLE domains. EXF provides the logic of multiplying scenarios and anchoring them in everyday lifeworlds. Scenario planning with critical uncertainties is used to generate distinct scenario spaces, such as tensions between surveillance and social divide. Interactive prototyping and field trial then embed these scenarios in ordinary settings, converting abstract futures into routine experience.

The insistence on multiplication is methodologically important. It prevents single-scenario lock-in and allows policy interpretation to remain portable across futures. When interventions are later translated into decision support, the recommendation is to create scenario-policy crosswalks that identify likely unintended consequences, values at stake, and possible levers such as certification, labeling, auditability, or household legibility tools.

4. Intervention repertoire and the smart-garment case

The best-developed case study concerns sustainable smart garment futures and data leakage at the intersection of circular economy ambitions and data privacy concerns. The objective is to anticipate unintended consequences of recycled electronics in wearable systems, elicit situated reactions to data leakage from circular flows, and generate policy-relevant insights before wide-scale adoption. The concrete intervention combines five Future Ripples workshops, scenario multiplication into four futures, iterative prototyping aligned with those scenarios, and a two-week in-home field trial built around a tracking garment and a stationary reflection device (Sanchez et al., 2023).

The intervention treats households as sites of pervasive experiential evidence. Participants wear the garment, encounter leakage episodes through their routines, and use the stationary device to reflect on perceived risks, disrupted habits, normalization, resignation, anger, or demands for protection. In policy terms, this is meant to reveal that privacy concerns are contextual and malleable rather than fixed. It also seeks to show under what conditions recycled electronics remain acceptable when data erasure and trust mechanisms are weak.

The stakeholder set is explicitly heterogeneous. Households provide situated value judgments and meaning-making practices. Policy designers and regulators are concerned with data privacy, circular economy policy, e-waste, consumer protection, and sustainability. HCI researchers and innovation teams facilitate workshops, prototype development, and analysis. The collision of STEEPLE factors is especially visible here: data privacy norms are not examined in isolation but against environmental imperatives around electronics recycling.

The policy outputs anticipated by design include identification of levers such as certified data-erasure standards for e-waste, labeling requirements, and home-device interfaces that improve legibility. The guidance also gives a concrete interpretive example: if households express anger when leakage involves health data but resignation when it involves fitness data, policy should differentiate data categories in circular flows and require context-sensitive protections. That example illustrates the intended translation from lived encounter to differentiated safeguard.

5. Evaluation, agility, and interpretation toward policy action

Experiential futures in-the-wild are described as agile evaluation settings. Agility comes from the parallel iteration of scenarios and prototypes, the use of time-bounded in-situ deployments such as two-week trials, and the use of lightweight workshops that surface consequences without heavy foresight infrastructure. This makes the method suitable for comparative learning across diverse households or contexts while retaining responsiveness to weak signals and stakeholder input (Sanchez et al., 2023).

To help policy-making assess impact, the evaluation can be organized around simple pseudo-formal measures aligned with RRI. The proposed quantities are anticipation evidence $A$, reflexivity episodes $R$, inclusion breadth $I$, and responsiveness actions $\mathrm{Resp}$. A composite informative impact score is then conceptualized as

$$\mathrm{ImpactScore} = w_1 \cdot A + w_2 \cdot R + w_3 \cdot I + w_4 \cdot \mathrm{Resp}$$

with weights tuned to policy priorities. The source explicitly notes that this is not a formalized standard, but a pragmatic way of tracking whether interventions are simultaneously anticipatory, reflective, inclusive, and action-oriented.

Interpretation is as important as measurement. The approach recommends using EXF’s “multiply” logic so that findings are not overgeneralized from one scenario. It also recommends explicit checks of representation across socio-cultural groups and explicit attention to whether safety, convenience, or sustainability goals are being privileged, and for whom. In this sense, the output is not merely a list of observed participant reactions; it is a structured translation of those reactions into responsive policy options framed by plural futures rather than a single preferred end state.

6. Benefits, risks, limitations, and relation to adjacent policy evidence traditions

The principal claimed benefit is that experiential futures in-the-wild bridge the policy-novelty gap by generating situated evidence before sociotechnical systems are stabilized. They surface everyday values and socio-ethical concerns that textual scenarios can miss, and they provide agile environments for policy experimentation aligned with RRI. These strengths are accompanied by clear risks: futures narratives can reflect bias, deployments simulating harms such as data leakage require stringent informed consent and participant protection, and pluralistic engagement demands time and resources if tokenism is to be avoided (Sanchez et al., 2023).

Several limitations are emphasized. Generalizing in-the-wild findings beyond local specificity is non-trivial. Test beds can be caught between open-ended experimentation and pressure to demonstrate success. Measuring policy impact rigorously across RRI dimensions still lacks strong instruments, so current practice remains pragmatic rather than econometric. There is also an unresolved responsibility question for HCI itself: if it empirically studies futures, it must also ensure that those futures can be translated across disciplinary boundaries into policy contexts.

Practical implementation requirements are correspondingly substantial. The method needs HCI field-trial expertise in ethical in-situ deployment and qualitative analysis, foresight facilitation capability, prototype-building capacity, and policy liaison work. Indicative timelines given for implementation are 6–10 weeks for foresight workshops and scenario development, 8–12 weeks for iterative prototyping, 2–4 weeks per in-the-wild deployment, and 6–10 weeks for analysis, RRI mapping, and policy option development.

Relative to adjacent policy evidence traditions, experiential futures in-the-wild occupy a distinctive position. Causal inference from administrative data addresses policy questions through estimands such as the average treatment effect and counterfactual quantities like $E[Y \mid do(T=t)]$, under assumptions encoded in DAGs and tested with diagnostics and sensitivity analysis (Tartaglia et al., 2023). Decision-theoretic probabilistic forecast design, as in monsoon forecasting for heterogeneous farmers, emphasizes calibrated and action-supportive probabilities when no single optimal action can be prescribed for all users (Aitken et al., 9 Mar 2026). This suggests that experiential futures are most distinctive where the policy problem concerns sociotechnical novelty, value conflict, and unintended consequences that are not yet observable in administrative records or forecast outcomes. In such settings, the method serves not as a substitute for causal or probabilistic evidence, but as a way to construct early, situated evidence about futures that policy may soon have to govern.