Participatory Modeling and Stakeholder Systems: Building Better Models with People

Last Updated June 7, 2026

Participatory modeling and stakeholder systems examine how models of complex systems can be built with the people, institutions, communities, practitioners, experts, and decision-makers who understand, affect, govern, or experience the system being modeled. Instead of treating modeling as a purely technical exercise conducted by analysts outside the system, participatory modeling treats model-building as a structured process of shared inquiry. It asks who defines the problem, whose knowledge counts, what assumptions are visible, how disagreement is handled, and how models can support learning, legitimacy, and better decisions.

Complex systems rarely belong to one discipline, one institution, or one data source. Water systems involve residents, engineers, regulators, utilities, farmers, ecosystems, emergency managers, and downstream communities. Public health systems involve patients, clinicians, administrators, insurers, public agencies, caregivers, data systems, and communities. Infrastructure systems involve operators, planners, maintenance crews, users, asset owners, contractors, regulators, and affected neighborhoods. Environmental systems involve scientists, landholders, Indigenous communities, policymakers, industries, advocacy groups, and species that cannot speak for themselves.

Participatory modeling matters because formal models do not merely represent systems. They also shape how systems are understood. A model can make some causes visible and others invisible. It can privilege quantitative data while excluding lived experience. It can define boundaries that include one group’s concerns and omit another’s. It can translate political disagreement into technical parameters. It can become a tool for public reasoning, or it can become a tool for exclusion.

The strongest participatory modeling processes do not ask stakeholders to rubber-stamp a model that has already been built. They involve stakeholders in framing the problem, defining boundaries, identifying variables, surfacing assumptions, testing scenarios, interpreting results, and deciding how model outputs should be used. The goal is not to replace technical rigor with opinion. The goal is to combine formal modeling with domain knowledge, local knowledge, institutional memory, practical experience, ethical judgment, and democratic legitimacy.

This article examines participatory modeling as a major direction in systems modeling. It covers stakeholder systems, problem framing, model co-design, group model building, companion modeling, mediated modeling, participatory system dynamics, participatory agent-based modeling, scenario workshops, knowledge integration, facilitation, power, legitimacy, uncertainty, ethics, mathematical framing, R and Python workflows, responsible use, common pitfalls, and authoritative references.

What Is Participatory Modeling?

Participatory modeling is a structured approach to building, using, and interpreting models with stakeholders rather than only for them. It brings affected groups, decision-makers, practitioners, experts, and modelers into the modeling process so that assumptions, boundaries, variables, scenarios, uncertainties, and interpretations can be examined collectively.

The word “participatory” does not mean that every stakeholder writes code or equations. Participation can take many forms: identifying the problem, mapping the system, reviewing causal diagrams, defining model boundaries, selecting scenarios, validating assumptions, interpreting outputs, challenging data sources, explaining institutional constraints, or deciding how results should be communicated.

Participatory modeling is especially valuable when systems are complex, contested, uncertain, and consequential. In those settings, technical models alone are not enough. The problem definition itself may be disputed. Data may be incomplete. Values may conflict. Institutions may have different incentives. Communities may have knowledge that is not present in formal datasets. Participatory modeling creates a process for making these differences visible.

The central claim of participatory modeling is not that stakeholders are always right or that technical analysis is secondary. The claim is that complex systems are better understood when formal models are informed by the knowledge, concerns, constraints, and values of those who live with or govern the system.

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Why Stakeholders Matter in Systems Modeling

Stakeholders matter because systems modeling always involves judgment. A modeler must decide what to include, what to exclude, what to measure, how to simplify, what scenarios matter, how uncertainty should be represented, and how results should be interpreted. These choices are not purely technical. They affect whose problems are recognized and whose concerns are left outside the model.

Stakeholders often hold knowledge that is essential for model credibility. Maintenance crews may understand infrastructure failure modes that asset databases miss. Residents may understand flood pathways that official maps understate. Clinicians may understand bottlenecks that health-system metrics conceal. Farmers may understand seasonal land and water constraints better than regional datasets. Community organizations may understand barriers to service access that are invisible in administrative records.

Participatory modeling helps integrate these forms of knowledge into formal model-building. The result is not automatic agreement. It is a more transparent process for seeing where knowledge aligns, where it conflicts, and where uncertainty remains.

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Stakeholder Systems and Model Boundaries

A stakeholder system is the network of actors, institutions, communities, authorities, interests, responsibilities, dependencies, and affected groups connected to the system being modeled. In participatory modeling, the stakeholder system is not peripheral. It is part of the system context.

Model boundaries determine what counts as relevant. A water model may include river flow and reservoir storage but exclude irrigation practices, land tenure, water rights, household access, ecosystem needs, or downstream communities. A transportation model may include congestion and route capacity but exclude disability access, fare burden, safety, emissions, or displacement. A public health model may include service capacity but exclude trust, language, insurance, caregiving, and administrative burden.

Boundary judgment is one of the most important points of participation. Stakeholders can reveal when a technically convenient boundary fails to match the lived, institutional, or ecological system.

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Participation vs Consultation

Participation is often confused with consultation. Consultation asks stakeholders to respond to work that has largely already been framed. Participation gives stakeholders meaningful influence over the modeling process itself. The difference matters because weak participation can create the appearance of inclusion without changing the model’s assumptions, boundaries, or uses.

Not every project requires the same level of participation. Some models need expert review. Others need stakeholder workshops. Some require community co-design, shared governance, or participatory scenario exploration. The appropriate level depends on the stakes, uncertainty, conflict, affected groups, decision authority, and consequences of model use.

Participatory modeling should be honest about the level of influence stakeholders actually have. Calling a process participatory when the key decisions are already fixed can damage trust and legitimacy.

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These approaches share a common principle: model-building is not only a computational act. It is also a social process of deciding what a system is, how it behaves, and what kinds of action are worth testing.

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The Participatory Modeling Process

A participatory modeling process usually moves through framing, stakeholder identification, knowledge elicitation, model design, scenario development, validation, interpretation, and use. The process may be linear, but in many projects it is iterative. Stakeholders may revise the problem frame after seeing an initial model. Modelers may discover that a key variable is poorly defined. Decision-makers may realize that a technically optimal scenario is politically infeasible or ethically unacceptable.

The process matters as much as the final model. A technically elegant model built through a poor process may lack legitimacy, miss important knowledge, or fail in implementation.

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Problem Framing and Boundary Negotiation

Problem framing is one of the most consequential parts of participatory modeling. The same situation can be framed in different ways. A city may frame flooding as a drainage capacity problem, while residents frame it as a housing, maintenance, insurance, and environmental justice problem. A hospital may frame congestion as a bed-capacity problem, while patients and staff frame it as a discharge planning, staffing, access, and administrative coordination problem.

Boundary negotiation asks what the model should include and exclude. This is not a purely technical decision. Boundaries determine responsibility, relevance, visibility, and interpretation. A narrow boundary can make a problem easier to model but less truthful. A broad boundary can improve realism but make modeling harder.

Participatory modeling does not guarantee that every concern can be included. It does make boundary choices visible and contestable.

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Stakeholder Knowledge and Model Structure

Stakeholder knowledge can shape model structure in several ways. It can identify variables, causal links, delays, thresholds, feedback loops, decision rules, behavioral assumptions, scenario conditions, data gaps, and validation criteria. It can also reveal when a model’s structure reflects institutional convenience rather than system reality.

For example, a model of emergency response may initially represent ambulance travel time and hospital capacity. Stakeholders may add dispatch rules, call triage, language barriers, neighborhood trust, road closures, weather, staffing shortages, and hospital diversion. These additions can change the model’s behavior and interpretation.

The challenge is translation. Stakeholder knowledge is often qualitative, narrative, experiential, or context-specific. The modeler’s task is not to flatten that knowledge into numbers too quickly, but to represent it carefully and document the assumptions involved.

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Group Model Building and Facilitated Modeling

Group model building is one of the most established participatory modeling approaches. It uses structured facilitation to help participants build shared representations of system structure. The process may use causal loop diagrams, stock-and-flow diagrams, behavior-over-time graphs, model boundary charts, variable elicitation, scenario discussions, or formal simulation models.

The facilitator’s role is critical. Participatory modeling can reproduce power imbalances if meetings are dominated by senior officials, technical experts, funders, or confident speakers. Good facilitation creates space for different forms of knowledge, clarifies disagreement, documents assumptions, prevents premature consensus, and separates model-building from advocacy.

Facilitated modeling is not merely a meeting technique. It is part of model quality. A model built through poor facilitation can encode social bias as technical structure.

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Scenario Workshops and Shared Exploration

Participatory modeling often uses scenario workshops to explore alternative futures, interventions, or policy choices. Stakeholders help define the scenarios, review assumptions, compare tradeoffs, and interpret outputs. This is especially useful when the future is uncertain, values conflict, or decisions must be made under deep uncertainty.

Scenario workshops can change how stakeholders understand the system. Participants may see that a preferred intervention performs poorly under certain conditions, that a technically efficient option imposes unacceptable burdens on one group, or that a politically modest intervention is more robust than a dramatic but fragile plan.

The best scenario workshops are not exercises in choosing the “right” scenario. They are structured opportunities to understand tradeoffs, uncertainty, disagreement, and resilience across plausible futures.

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Power, Conflict, and Representation

Participatory modeling can reveal power, but it can also reproduce it. Stakeholders do not enter modeling processes with equal authority, time, resources, technical fluency, institutional protection, or freedom to disagree. Some participants may have legal authority. Others may have lived experience but little formal influence. Some may speak confidently in public meetings. Others may face language, disability, digital, cultural, or trust barriers.

Conflict is not a sign that participatory modeling has failed. In complex systems, disagreement often reflects real differences in experience, values, risk exposure, and institutional position. The goal is not to erase conflict. The goal is to represent it honestly and prevent the model from hiding it behind technical language.

A participatory model is not legitimate simply because people were in the room. Legitimacy depends on who was included, how power was handled, what influence participation had, and how the model is used.

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Legitimacy, Trust, and Model Ownership

Participatory modeling can improve trust when stakeholders understand how the model works, see their knowledge represented, can challenge assumptions, and know how outputs will be used. But participation can also damage trust if it feels performative, extractive, rushed, or predetermined.

Model ownership is not only legal ownership. It includes practical and interpretive ownership: who can explain the model, update it, question it, access outputs, decide scenarios, maintain documentation, and use results in public or institutional settings.

Trust is built through process design, not through claims of neutrality. A model becomes more legitimate when stakeholders can understand, question, and influence it.

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Data Governance and Participatory Evidence

Participatory modeling often brings together formal datasets and stakeholder evidence. Formal data may include sensor records, administrative data, surveys, geospatial layers, budgets, service records, health data, environmental monitoring, or economic indicators. Stakeholder evidence may include lived experience, local observation, oral history, institutional memory, field practice, community mapping, and professional judgment.

These evidence forms should not be treated as interchangeable. Each has strengths and limits. Administrative data may be systematic but biased by eligibility rules. Local knowledge may be context-rich but difficult to generalize. Sensor data may be precise but spatially incomplete. Expert judgment may be deep but shaped by disciplinary assumptions.

Participatory evidence governance should answer a simple question: who has authority over the knowledge used in the model, and who benefits from its use?

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Applications of Participatory Modeling

Participatory modeling is especially useful in public, environmental, organizational, and social systems where technical complexity overlaps with contested values, distributed authority, and implementation challenges. It helps connect model logic to the people and institutions that must interpret and act on the model.

Across these domains, participatory modeling is most valuable when no single actor has enough knowledge, authority, or legitimacy to define the system alone.

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Relationship to Other Systems Modeling Approaches

Participatory modeling can be combined with nearly every major systems modeling paradigm. It is not a replacement for technical modeling. It is a way to improve problem framing, model structure, scenario design, validation, interpretation, and legitimacy.

Participatory modeling strengthens systems modeling by making the social life of models explicit: who builds them, who understands them, who contests them, and who is affected by their use.

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Mathematical Lens: Stakeholder Weights, Scenario Preferences, and Shared Uncertainty

A participatory model can represent multiple stakeholder perspectives by assigning stakeholder groups to a set \(G\):

Each stakeholder group may assign importance weights to model outcomes:

A scenario score for stakeholder group \(g\) can be represented as a weighted outcome sum:

Disagreement across stakeholders can be summarized as dispersion in scenario scores:

Shared uncertainty can be represented as an interval around a model outcome:

A legitimacy-adjusted scenario evaluation can include both performance and disagreement:

These equations do not make stakeholder judgment mechanical. They show how participatory modeling can make values, disagreement, uncertainty, and tradeoffs explicit rather than hiding them inside a single technical score.

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Strengths and Limitations

Participatory modeling can improve learning, legitimacy, relevance, and implementation. It can also be time-consuming, politically difficult, and vulnerable to weak facilitation or token engagement. It should not be romanticized. Participation is powerful only when it is designed with care.

The central limitation is that participation does not automatically produce justice, accuracy, or consensus. It creates a process where those questions can be examined more openly.

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R Workflow: Stakeholder Scenario Scoring and Consensus Diagnostics

The R workflow below uses base R only. It creates synthetic stakeholder groups, outcome priorities, scenario performance values, stakeholder-specific scenario scores, disagreement diagnostics, and legitimacy-adjusted scenario rankings. It is intentionally dependency-light so it can run in a clean environment.

# participatory_modeling_workflow.R
# Base R workflow:
# stakeholder scenario scoring and consensus diagnostics.
#
# Suggested repository placement:
# articles/participatory-modeling-and-stakeholder-systems/r/participatory_modeling_workflow.R

args <- commandArgs(trailingOnly = FALSE)
file_arg <- grep("^--file=", args, value = TRUE)

if (length(file_arg) > 0) {
  script_path <- normalizePath(sub("^--file=", "", file_arg[1]), mustWork = TRUE)
  article_root <- normalizePath(file.path(dirname(script_path), ".."), mustWork = TRUE)
} else {
  article_root <- normalizePath(getwd(), mustWork = TRUE)
}

tables_dir <- file.path(article_root, "outputs", "tables")
figures_dir <- file.path(article_root, "outputs", "figures")

dir.create(tables_dir, recursive = TRUE, showWarnings = FALSE)
dir.create(figures_dir, recursive = TRUE, showWarnings = FALSE)

stakeholders <- data.frame(
  stakeholder_group = c(
    "community_residents",
    "frontline_staff",
    "technical_experts",
    "public_agency",
    "service_users",
    "resource_managers"
  ),
  access_weight = c(0.30, 0.20, 0.15, 0.20, 0.35, 0.15),
  cost_weight = c(0.10, 0.15, 0.20, 0.25, 0.10, 0.20),
  resilience_weight = c(0.20, 0.25, 0.30, 0.25, 0.15, 0.30),
  equity_weight = c(0.30, 0.20, 0.15, 0.15, 0.30, 0.15),
  feasibility_weight = c(0.10, 0.20, 0.20, 0.15, 0.10, 0.20)
)

scenarios <- data.frame(
  scenario = c(
    "targeted_service_expansion",
    "infrastructure_repair_priority",
    "digital_monitoring_platform",
    "community_led_resilience",
    "baseline_policy_continuation"
  ),
  access = c(0.85, 0.55, 0.60, 0.75, 0.40),
  cost = c(0.55, 0.65, 0.50, 0.70, 0.90),
  resilience = c(0.65, 0.85, 0.70, 0.80, 0.35),
  equity = c(0.90, 0.50, 0.45, 0.85, 0.30),
  feasibility = c(0.60, 0.75, 0.70, 0.55, 0.85)
)

score_rows <- data.frame()

for (i in seq_len(nrow(stakeholders))) {
  for (j in seq_len(nrow(scenarios))) {
    score <-
      stakeholders$access_weight[i] * scenarios$access[j] +
      stakeholders$cost_weight[i] * scenarios$cost[j] +
      stakeholders$resilience_weight[i] * scenarios$resilience[j] +
      stakeholders$equity_weight[i] * scenarios$equity[j] +
      stakeholders$feasibility_weight[i] * scenarios$feasibility[j]

    score_rows <- rbind(
      score_rows,
      data.frame(
        stakeholder_group = stakeholders$stakeholder_group[i],
        scenario = scenarios$scenario[j],
        score = score
      )
    )
  }
}

scenario_summary <- aggregate(
  score ~ scenario,
  data = score_rows,
  FUN = function(x) c(mean = mean(x), sd = sd(x), min = min(x), max = max(x))
)

scenario_summary <- do.call(data.frame, scenario_summary)
names(scenario_summary) <- c(
  "scenario",
  "mean_score",
  "disagreement_sd",
  "minimum_score",
  "maximum_score"
)

lambda <- 0.50
scenario_summary$legitimacy_adjusted_score <-
  scenario_summary$mean_score - lambda * scenario_summary$disagreement_sd

scenario_summary <- scenario_summary[order(-scenario_summary$legitimacy_adjusted_score), ]

validation_checks <- data.frame(
  check = c(
    "stakeholder_weights_sum_to_one",
    "scenario_scores_between_zero_and_one",
    "scenario_summary_created"
  ),
  passed = c(
    all(abs(rowSums(stakeholders[, 2:6]) - 1) < 1e-9),
    all(score_rows$score >= 0 & score_rows$score <= 1),
    nrow(scenario_summary) > 0
  )
)

write.csv(
  stakeholders,
  file.path(tables_dir, "r_participatory_stakeholder_weights.csv"),
  row.names = FALSE
)

write.csv(
  scenarios,
  file.path(tables_dir, "r_participatory_scenarios.csv"),
  row.names = FALSE
)

write.csv(
  score_rows,
  file.path(tables_dir, "r_participatory_stakeholder_scenario_scores.csv"),
  row.names = FALSE
)

write.csv(
  scenario_summary,
  file.path(tables_dir, "r_participatory_scenario_summary.csv"),
  row.names = FALSE
)

write.csv(
  validation_checks,
  file.path(tables_dir, "r_participatory_validation_checks.csv"),
  row.names = FALSE
)

png(file.path(figures_dir, "r_participatory_scenario_scores.png"), width = 1000, height = 700)
barplot(
  scenario_summary$legitimacy_adjusted_score,
  names.arg = scenario_summary$scenario,
  las = 2,
  ylab = "Legitimacy-Adjusted Score",
  main = "Participatory Scenario Comparison"
)
grid()
dev.off()

print(scenario_summary)
print(validation_checks)
cat("R participatory modeling workflow complete.\n")

This workflow demonstrates how stakeholder priorities can be represented transparently without pretending that values are purely technical. A professional process would include facilitated elicitation, uncertainty intervals, dissent tracking, qualitative interpretation, and governance agreements around model use.

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Python Workflow: Participatory Model Assumption Register and Scenario Comparison

The Python workflow below uses only the standard library. It creates stakeholder groups, outcome weights, scenario scores, assumption registers, disagreement diagnostics, and validation checks. It exports tables that can support a participatory modeling workshop or documentation package.

#!/usr/bin/env python3
"""
Participatory modeling and stakeholder systems workflow.

Dependency-light workflow demonstrating:

1. Stakeholder group definitions
2. Outcome weights
3. Scenario performance values
4. Stakeholder-specific scenario scoring
5. Disagreement diagnostics
6. Assumption register
7. Validation checks

All data are synthetic.
"""

from __future__ import annotations

from pathlib import Path
import csv
import math
from statistics import mean, pstdev


ARTICLE_ROOT = Path(__file__).resolve().parents[1]
TABLES = ARTICLE_ROOT / "outputs" / "tables"


def write_csv(path: Path, rows: list[dict[str, object]]) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    if not rows:
        raise ValueError(f"No rows to write: {path}")

    fieldnames: list[str] = []
    for row in rows:
        for key in row:
            if key not in fieldnames:
                fieldnames.append(key)

    with path.open("w", newline="", encoding="utf-8") as handle:
        writer = csv.DictWriter(handle, fieldnames=fieldnames, extrasaction="ignore")
        writer.writeheader()
        writer.writerows(rows)


def main() -> None:
    stakeholders = [
        {
            "stakeholder_group": "community_residents",
            "access": 0.30,
            "cost": 0.10,
            "resilience": 0.20,
            "equity": 0.30,
            "feasibility": 0.10,
        },
        {
            "stakeholder_group": "frontline_staff",
            "access": 0.20,
            "cost": 0.15,
            "resilience": 0.25,
            "equity": 0.20,
            "feasibility": 0.20,
        },
        {
            "stakeholder_group": "technical_experts",
            "access": 0.15,
            "cost": 0.20,
            "resilience": 0.30,
            "equity": 0.15,
            "feasibility": 0.20,
        },
        {
            "stakeholder_group": "public_agency",
            "access": 0.20,
            "cost": 0.25,
            "resilience": 0.25,
            "equity": 0.15,
            "feasibility": 0.15,
        },
        {
            "stakeholder_group": "service_users",
            "access": 0.35,
            "cost": 0.10,
            "resilience": 0.15,
            "equity": 0.30,
            "feasibility": 0.10,
        },
        {
            "stakeholder_group": "resource_managers",
            "access": 0.15,
            "cost": 0.20,
            "resilience": 0.30,
            "equity": 0.15,
            "feasibility": 0.20,
        },
    ]

    scenarios = [
        {
            "scenario": "targeted_service_expansion",
            "access": 0.85,
            "cost": 0.55,
            "resilience": 0.65,
            "equity": 0.90,
            "feasibility": 0.60,
        },
        {
            "scenario": "infrastructure_repair_priority",
            "access": 0.55,
            "cost": 0.65,
            "resilience": 0.85,
            "equity": 0.50,
            "feasibility": 0.75,
        },
        {
            "scenario": "digital_monitoring_platform",
            "access": 0.60,
            "cost": 0.50,
            "resilience": 0.70,
            "equity": 0.45,
            "feasibility": 0.70,
        },
        {
            "scenario": "community_led_resilience",
            "access": 0.75,
            "cost": 0.70,
            "resilience": 0.80,
            "equity": 0.85,
            "feasibility": 0.55,
        },
        {
            "scenario": "baseline_policy_continuation",
            "access": 0.40,
            "cost": 0.90,
            "resilience": 0.35,
            "equity": 0.30,
            "feasibility": 0.85,
        },
    ]

    outcomes = ["access", "cost", "resilience", "equity", "feasibility"]

    score_rows: list[dict[str, object]] = []

    for stakeholder in stakeholders:
        for scenario in scenarios:
            score = sum(float(stakeholder[outcome]) * float(scenario[outcome]) for outcome in outcomes)

            score_rows.append({
                "stakeholder_group": stakeholder["stakeholder_group"],
                "scenario": scenario["scenario"],
                "score": round(score, 6),
            })

    scenario_names = [str(item["scenario"]) for item in scenarios]
    summary_rows: list[dict[str, object]] = []

    for scenario_name in scenario_names:
        scores = [
            float(row["score"])
            for row in score_rows
            if row["scenario"] == scenario_name
        ]

        mean_score = mean(scores)
        disagreement_sd = pstdev(scores)
        minimum_score = min(scores)
        maximum_score = max(scores)
        legitimacy_adjusted_score = mean_score - 0.50 * disagreement_sd

        summary_rows.append({
            "scenario": scenario_name,
            "mean_score": round(mean_score, 6),
            "disagreement_sd": round(disagreement_sd, 6),
            "minimum_score": round(minimum_score, 6),
            "maximum_score": round(maximum_score, 6),
            "legitimacy_adjusted_score": round(legitimacy_adjusted_score, 6),
        })

    summary_rows.sort(key=lambda row: float(row["legitimacy_adjusted_score"]), reverse=True)

    assumption_rows = [
        {
            "assumption_id": "A1",
            "assumption": "Stakeholder outcome weights sum to one within each group.",
            "source": "workshop_elicitation",
            "status": "documented",
            "risk_if_wrong": "Scenario rankings may overstate consensus.",
        },
        {
            "assumption_id": "A2",
            "assumption": "Scenario performance values are normalized from zero to one.",
            "source": "modeler_translation",
            "status": "needs_review",
            "risk_if_wrong": "Scores may compare unlike quantities.",
        },
        {
            "assumption_id": "A3",
            "assumption": "Disagreement is represented as score dispersion across stakeholder groups.",
            "source": "model_design",
            "status": "documented",
            "risk_if_wrong": "Important qualitative disagreement may be hidden.",
        },
        {
            "assumption_id": "A4",
            "assumption": "Legitimacy-adjusted score penalizes high disagreement.",
            "source": "participatory_design_choice",
            "status": "contested",
            "risk_if_wrong": "The process may overvalue consensus or undervalue principled disagreement.",
        },
        {
            "assumption_id": "A5",
            "assumption": "All relevant stakeholder groups are represented in the scoring table.",
            "source": "stakeholder_mapping",
            "status": "needs_review",
            "risk_if_wrong": "Missing groups may invalidate the participatory claim.",
        },
    ]

    validation_rows = [
        {
            "check": "stakeholder_weights_sum_to_one",
            "passed": all(
                math.isclose(sum(float(stakeholder[outcome]) for outcome in outcomes), 1.0, abs_tol=1e-9)
                for stakeholder in stakeholders
            ),
            "value": "all_groups_checked",
        },
        {
            "check": "scenario_scores_between_zero_and_one",
            "passed": all(0.0 <= float(row["score"]) <= 1.0 for row in score_rows),
            "value": "all_scores_checked",
        },
        {
            "check": "scenario_summary_created",
            "passed": len(summary_rows) == len(scenarios),
            "value": len(summary_rows),
        },
        {
            "check": "assumption_register_created",
            "passed": len(assumption_rows) > 0,
            "value": len(assumption_rows),
        },
    ]

    write_csv(TABLES / "python_participatory_stakeholder_weights.csv", stakeholders)
    write_csv(TABLES / "python_participatory_scenarios.csv", scenarios)
    write_csv(TABLES / "python_participatory_stakeholder_scenario_scores.csv", score_rows)
    write_csv(TABLES / "python_participatory_scenario_summary.csv", summary_rows)
    write_csv(TABLES / "python_participatory_assumption_register.csv", assumption_rows)
    write_csv(TABLES / "python_participatory_validation_checks.csv", validation_rows)

    print("Participatory modeling workflow complete.")
    print(TABLES / "python_participatory_scenario_summary.csv")


if __name__ == "__main__":
    main()

This workflow demonstrates a core participatory modeling idea: values, assumptions, disagreement, and scenario preferences can be documented rather than hidden. The model does not decide what is legitimate. It creates a transparent structure for discussing legitimacy.

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GitHub Repository

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Common Pitfalls

Participatory modeling can fail when participation is symbolic, poorly facilitated, technically opaque, extractive, or disconnected from actual decisions. The strongest participatory models are honest about power, uncertainty, disagreement, and the limits of stakeholder influence.

The central correction is to design participation as part of model quality, not as a public-relations layer added after technical decisions have already been made.

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Conclusion

Participatory modeling and stakeholder systems matter because complex systems cannot be understood responsibly through technical representation alone. Models are built from assumptions, boundaries, variables, evidence, values, scenarios, and interpretations. Stakeholders can help reveal where those choices are incomplete, contested, unrealistic, or unjust.

Participatory modeling does not eliminate technical rigor. It changes the conditions under which rigor is achieved. A model can be mathematically precise and socially irrelevant. It can be computationally advanced and institutionally unusable. It can be data-rich and ethically weak. Participatory modeling helps connect formal analysis to the people, institutions, communities, and practical knowledge that determine whether a model is meaningful.

The best participatory models support shared learning without pretending that participation automatically creates consensus. They make disagreement visible. They document assumptions. They expose boundary choices. They help stakeholders test scenarios and interpret uncertainty. They clarify who benefits, who bears risk, and what decisions remain unresolved.

In systems modeling, participation is not a soft add-on. It is a method for improving model relevance, legitimacy, accountability, and public reasoning. When the system being modeled affects real people, those people should not be treated as external to the model. They are part of the system the model is trying to understand.

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Related Articles

• Systems Modeling
• What Is Systems Modeling?
• Public Policy Modeling
• Organizational Systems Modeling
• Health Systems Modeling
• Geospatial Systems Modeling
• Model Assumptions and Boundary Judgment
• When Systems Models Clarify and When They Distort
• Ethics, Power, and Systems Modeling
• Communicating Model Results Responsibly
• Scenario Modeling and Simulation
• Systems Thinking vs Systems Modeling

Further Reading

• Voinov, A. and Bousquet, F. (2010) ‘Modelling with stakeholders’, Environmental Modelling & Software, 25(11), pp. 1268–1281. Publication information available at: https://research.utwente.nl/en/publications/modelling-with-stakeholders-2/.
• Voinov, A., et al. (2016) ‘Modelling with stakeholders — Next generation’, Environmental Modelling & Software, 77, pp. 196–220. USGS record available at: https://pubs.usgs.gov/publication/70187144.
• Étienne, M. (ed.) (2014) Companion Modelling: A Participatory Approach to Support Sustainable Development. Dordrecht: Springer. Available at: https://link.springer.com/book/10.1007/978-94-017-8557-0.
• Hovmand, P.S. (2014) Community Based System Dynamics. New York: Springer.
• Vennix, J.A.M. (1996) Group Model Building: Facilitating Team Learning Using System Dynamics. Chichester: Wiley.
• Vennix, J.A.M. (1999) ‘Group model-building: Tackling messy problems’, System Dynamics Review, 15(4), pp. 379–401.
• van den Belt, M. (2004) Mediated Modeling: A System Dynamics Approach to Environmental Consensus Building. Washington, DC: Island Press.
• Checkland, P. and Scholes, J. (1990) Soft Systems Methodology in Action. Chichester: Wiley.
• Rouwette, E.A.J.A., Vennix, J.A.M. and Mullekom, T. van (2002) ‘Group model building effectiveness: A review of assessment studies’, System Dynamics Review, 18(1), pp. 5–45.
• ComMod. The Companion Modelling Approach. Available at: https://www.commod.org/en.
• System Dynamics Society. System Dynamics Society. Available at: https://systemdynamics.org/.
• International Association for Public Participation. IAP2 Public Participation Spectrum. Available at: https://iap2.org.au/resources/spectrum/.

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References

• Checkland, P. and Scholes, J. (1990) Soft Systems Methodology in Action. Chichester: Wiley.
• ComMod. (n.d.) The Companion Modelling Approach. Available at: https://www.commod.org/en.
• Étienne, M. (ed.) (2014) Companion Modelling: A Participatory Approach to Support Sustainable Development. Dordrecht: Springer. Available at: https://link.springer.com/book/10.1007/978-94-017-8557-0.
• Hovmand, P.S. (2014) Community Based System Dynamics. New York: Springer.
• Rouwette, E.A.J.A., Vennix, J.A.M. and Mullekom, T. van (2002) ‘Group model building effectiveness: A review of assessment studies’, System Dynamics Review, 18(1), pp. 5–45.
• System Dynamics Society. (n.d.) System Dynamics Society. Available at: https://systemdynamics.org/.
• van den Belt, M. (2004) Mediated Modeling: A System Dynamics Approach to Environmental Consensus Building. Washington, DC: Island Press.
• Vennix, J.A.M. (1996) Group Model Building: Facilitating Team Learning Using System Dynamics. Chichester: Wiley.
• Vennix, J.A.M. (1999) ‘Group model-building: Tackling messy problems’, System Dynamics Review, 15(4), pp. 379–401.
• Voinov, A. and Bousquet, F. (2010) ‘Modelling with stakeholders’, Environmental Modelling & Software, 25(11), pp. 1268–1281. Publication information available at: https://research.utwente.nl/en/publications/modelling-with-stakeholders-2/.
• Voinov, A., et al. (2016) ‘Modelling with stakeholders — Next generation’, Environmental Modelling & Software, 77, pp. 196–220. USGS record available at: https://pubs.usgs.gov/publication/70187144.

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