
Foundations: value and limits
Separate public goods, risk transfer, and non-substitutable limits before monetising nature.
FRONTIER ECONOMICS LAB
Nature positive is economics.
Choose a question, explore the theory, and test it in a game. Learn by asking what changes when one condition moves—not by memorising answers.
01 · QUESTIONS
These are not levels. Choose the question closest to what you want to understand now.

Separate public goods, risk transfer, and non-substitutable limits before monetising nature.

Turn good intentions into rules that survive reporting, participation, and renegotiation.

Treat probability, model doubt, evidence cost, and the value of waiting separately.

Read coalitions, spatial links, local rules, and the evolution of cooperation.
THEORIES
Open a card to connect key ideas, a regional example, an experiential mission, and a primary source.
ECOLOGICAL FEEDBACKHow does cooperation today change tomorrow’s ecology and payoff to cooperate?
This field combines public-good foundations with dynamic environmental economics; the names below represent its foundations and dynamic development rather than a single inventor.
Showed mathematically how much of a pure public good society should provide for efficient allocation.
Studied how cooperation today changes accumulated public value and later choices in repeated dynamic public-good games.
Eₜ₊₁ = Eₜ + r(Eₜ) + Σᵢcᵢ,ₜ − dₜIn plain words: Next ecology = current ecology + recovery + contributions − degradationEₜ / Eₜ₊₁r(Eₜ)Σᵢcᵢ,ₜdₜDelay can degrade nature and raise the cost of cooperation in the next round; the feedback loop is what makes the model dynamic.
Dynamic public-goods games with ecological feedback gives you a practical way to ask: How does cooperation today change tomorrow’s ecology and payoff to cooperate? Start with the familiar case below, identify the condition that drives the result, and then test that condition in the linked exercise. A one-period forest-restoration delay raises fire loss and can make a downstream beneficiary’s next contribution irrational.
A one-period forest-restoration delay raises fire loss and can make a downstream beneficiary’s next contribution irrational.
At the destination, choose a role, set the Restoration amount, and commit each round. Use a low restoration amount once and raise it later, then compare the displayed forest integrity and trust with Loss and Payout in the round history.
Predict the result before opening the link; after operating the control, explain the difference in one sentence.Open the forest 20-year game
IC / IRIs truthful reporting and continued participation rational for every actor?
These researchers developed mechanism design: rules that encourage truthful reporting and participation even when people hold private information.
Laid the foundations of designing institutions backward from desired outcomes.
Showed when rules can actually produce a stated social goal, the field known as implementation theory.
Generalized rules that make truth-telling more rewarding than lying, known as incentive compatibility.
Uᵢ(truth) ≥ Uᵢ(lie), Uᵢ(join) ≥ Uᵢ(outside)In plain words: Utility from truth is at least utility from lying; joining is at least as good as staying outUᵢtruth / liejoin / outsideValues 48–88 are a hypothetical 0–100 utility index for explanation, not observed data.
The left pair makes truth better than lying (incentive compatibility, IC); the right makes joining better than leaving (individual rationality, IR). A workable mechanism needs both.
Dynamic mechanism design gives you a practical way to ask: Is truthful reporting and continued participation rational for every actor? Start with the familiar case below, identify the condition that drives the result, and then test that condition in the linked exercise. Set payments so farmers do not overstate crop risk and still join prevention and index cover.
Set payments so farmers do not overstate crop risk and still join prevention and index cover.
“From the field, I would begin by asking: Is truthful reporting and continued participation rational for every actor?”
“The theoretical core is this: Design for private information and outside options, not only the social objective.”
“For finance and contracts, turn this into an explicit decision rule: Test incentive compatibility and individual rationality even when total value is positive. The case shows why: Set payments so farmers do not overstate crop risk and still join prevention and index cover.”
“But whose knowledge and whose future are missing when we frame the problem only as “Is truthful reporting and continued participation rational for every actor?”? Keep room to revise the institution.”
At the destination, work through the four decisions. At the contract decision, compare all three rules, choose the design separating upfront finance, independent MRV, outcome payment, and community conditions, then read why the other rules weaken participation or truthful reporting.
Predict the result before opening the link; after operating the control, explain the difference in one sentence.Open the outcome-bond game
COALITIONS / CORECould a subset of actors form a better coalition?
These scholars shaped cooperative game theory: how to divide shared value without giving a smaller group reason to leave.
Systematized game theory and its early treatment of coalitions.
Proposed the Shapley value: average the extra value each person adds across every joining order.
Formalized the core: allocations where no smaller group gains by leaving and acting alone.
Σᵢxᵢ = v(N), Σᵢ∈S xᵢ ≥ v(S)In plain words: Allocate all grand-coalition value and give every sub-coalition at least its outside valuexᵢN / Sv(N)v(S)50, 70, and 100 are hypothetical value units used to show a breakaway condition, not real project amounts.
Even if all 100 units are allocated, a pair receiving 50 will leave if it can create 70 on its own.
Cooperative game theory gives you a practical way to ask: Could a subset of actors form a better coalition? Start with the familiar case below, identify the condition that drives the result, and then test that condition in the linked exercise. Forest owners and a downstream utility may leave a grand coalition that includes local workers.
Forest owners and a downstream utility may leave a grand coalition that includes local workers.
“From the field, I would begin by asking: Could a subset of actors form a better coalition?”
“The theoretical core is this: A fair-looking Shapley allocation and core stability without breakaway coalitions are different properties.”
“For finance and contracts, turn this into an explicit decision rule: An allocation below an actor’s outside option is not implementable despite high total value. The case shows why: Forest owners and a downstream utility may leave a grand coalition that includes local workers.”
“But whose knowledge and whose future are missing when we frame the problem only as “Could a subset of actors form a better coalition?”? Keep room to revise the institution.”
At the destination, continue to Compose cooperation, compare the three allocation rules, choose the Shapley starting point with individual-rationality and core checks, and confirm the decision to inspect when no actor or subgroup wants to leave.
Predict the result before opening the link; after operating the control, explain the difference in one sentence.Test allocation in the watershed game
RESIDUAL CONTROLWho ultimately decides when an event was not written into the contract?
These scholars developed incomplete-contract theory: when not every future event can be written down, who holds final control changes investment.
Showed that when a contract cannot list every future event, who holds final control changes investment beforehand.
With Hart, formalized how ownership carries the right to decide in situations the contract did not specify.
Studied what happens when unforeseen events force renegotiation among people with different control rights.
Written states ⊂ Possible states → residual control rightsIn plain words: Written contingencies cover only part of reality; residual control governs the restWritten statesPossible statesresidual control rightsWhen an unforeseen shock occurs, the holder of residual control—not a missing clause—sets the path forward.
Incomplete contracts and property rights gives you a practical way to ask: Who ultimately decides when an event was not written into the contract? Start with the familiar case below, identify the condition that drives the result, and then test that condition in the linked exercise. After extreme fire invalidates the plan, community, owner, and insurer dispute recovery order.
After extreme fire invalidates the plan, community, owner, and insurer dispute recovery order.
“From the field, I would begin by asking: Who ultimately decides when an event was not written into the contract?”
“The theoretical core is this: Because not every event can be specified, residual control shapes ex-ante investment.”
“For finance and contracts, turn this into an explicit decision rule: Do not collapse ownership, use, renegotiation, and grievance into one generic ‘right’. The case shows why: After extreme fire invalidates the plan, community, owner, and insurer dispute recovery order.”
“But whose knowledge and whose future are missing when we frame the problem only as “Who ultimately decides when an event was not written into the contract?”? Keep room to revise the institution.”
At the destination, open STEP 06, expand authority and MRV, change the Decision authority to another actor, check rights-holder consent, and update the decision. Compare the resulting stop reason or next action.
Predict the result before opening the link; after operating the control, explain the difference in one sentence.Open the forest Workspace
ATTENTION BUDGETDoes more observation actually improve the decision?
This theory treats attention as scarce: collecting information is not enough when people can process only a limited amount.
Brought rational inattention into economics by treating limited attention as a choice over which information to process.
Extended rational inattention to concrete choices among products or actions when attention is limited.
max E[u(a,θ)] − λ I(θ;s)In plain words: Maximize expected decision value minus the attention cost of processing informationaθsI(θ;s)λIf attention can process only two of six signals, prioritize those that can change the action.
Rational inattention and information economics gives you a practical way to ask: Does more observation actually improve the decision? Start with the familiar case below, identify the condition that drives the result, and then test that condition in the linked exercise. Compare another farm sensor with the foregone benefit of prevention or local employment.
Compare another farm sensor with the foregone benefit of prevention or local employment.
“From the field, I would begin by asking: Does more observation actually improve the decision?”
“The theoretical core is this: Information consumes both acquisition money and scarce human attention.”
“For finance and contracts, turn this into an explicit decision rule: Choose MRV by the marginal value of changing a decision, not maximum precision. The case shows why: Compare another farm sensor with the foregone benefit of prevention or local employment.”
“But whose knowledge and whose future are missing when we frame the problem only as “Does more observation actually improve the decision?”? Keep room to revise the institution.”
At the destination, open STEP 08 and change one observation option's Cost and Uncertainty reduction (%). Press Update decision in the header, see whether its priority changes, and choose by decision value and burden rather than precision alone.
Predict the result before opening the link; after operating the control, explain the difference in one sentence.Open the agriculture Workspace
ROBUSTNESSWill the design survive when the probability model itself is wrong?
These researchers distinguish known risk from model ambiguity and develop robust choice under misspecification.
Distinguished risk with known odds from uncertainty where even the odds are unclear, motivating ambiguity aversion.
Formalized maxmin expected utility: compare each action under its worst plausible probability model, then choose the best of those worst cases.
Developed robust control: decisions designed not to fail badly when the chosen model is somewhat wrong.
a* = arg maxₐ minₚ∈𝒫 Eₚ[u(a)]In plain words: Choose the action with the best worst-case expected utility across plausible modelsa / a*𝒫pEₚ[u(a)]35, 62, and 49 are hypothetical 0–100 outcome scores for explanation, not forecasts or observations.
Plan A may have the best average, yet robust choice can favor B when A has a much lower model-dependent floor.
Ambiguity, misspecification, and robust choice gives you a practical way to ask: Will the design survive when the probability model itself is wrong? Start with the familiar case below, identify the condition that drives the result, and then test that condition in the linked exercise. Combine parametric cover and a shared reserve when reef loss departs from history.
Combine parametric cover and a shared reserve when reef loss departs from history.
“From the field, I would begin by asking: Will the design survive when the probability model itself is wrong?”
“The theoretical core is this: Separate risk with known probabilities from ambiguity about the model.”
“For finance and contracts, turn this into an explicit decision rule: Use multiple models or minimax regret to inspect downside robustness. The case shows why: Combine parametric cover and a shared reserve when reef loss departs from history.”
“But whose knowledge and whose future are missing when we frame the problem only as “Will the design survive when the probability model itself is wrong?”? Keep room to revise the institution.”
At the destination, continue to Compare counterfactuals, read P10 and maximum regret for all three options, remove any option breaching the safety floor, choose the lowest-regret remaining option, and confirm the decision.
Predict the result before opening the link; after operating the control, explain the difference in one sentence.Test robustness in the watershed game
OPTION VALUEWhat is the value of preserving room to learn before scaling?
These scholars extended option thinking to irreversible real investment: waiting for new information before committing can itself be valuable.
Popularized real options: the right to invest later after seeing new conditions can itself have value.
Systematized when to invest, wait, or exit when a decision is costly to reverse, the problem of irreversibility.
F(V) = max{V − I, E[e^(−rΔt) F(V′)]}In plain words: Compare investing now with the discounted expected value of keeping the option and waitingF(V)V − IV′r / Δte^(−rΔt)E[·]A pilot preserves the option to expand, adapt, or stop, but ecological degradation during the wait remains a real cost.
Real options and irreversibility gives you a practical way to ask: What is the value of preserving room to learn before scaling? Start with the familiar case below, identify the condition that drives the result, and then test that condition in the linked exercise. Compare immediate full wetland restoration with a learn-then-scale pilot.
Compare immediate full wetland restoration with a learn-then-scale pilot.
“From the field, I would begin by asking: What is the value of preserving room to learn before scaling?”
“The theoretical core is this: With irreversible investment or loss, staging preserves the value of future information.”
“For finance and contracts, turn this into an explicit decision rule: Waiting is not free: count ecological degradation and unavoidable loss during delay. The case shows why: Compare immediate full wetland restoration with a learn-then-scale pilot.”
“But whose knowledge and whose future are missing when we frame the problem only as “What is the value of preserving room to learn before scaling?”? Keep room to revise the institution.”
At the destination, finish the story, open the coastal-portfolio question, compare the fastest-completion option with the long-term habitat-safe portfolio, select an answer and confidence, and confirm the decision to inspect timing and retained choices.
Predict the result before opening the link; after operating the control, explain the difference in one sentence.Open the coastal-restoration game
SPATIAL NETWORKSWhich connected places or actors create more-than-additive system value?
These researchers studied network effects: how value changes with connections and how those effects spread beyond one user or place.
Analyzed network externalities: a user's value changes as more compatible users or connections join.
Studied the benefits of compatible standards and why established networks can be difficult to leave.
Developed economic analysis of how who connects to whom changes individual and system-wide outcomes.
Connectivity value ≠ Σ parcel valuesIn plain words: System value depends on links among parcels, not only the sum of parcel valuesΣ parcel valuesConnectivity value≠28 and 86 are a hypothetical 0–100 connectivity index for explanation, not observed values.
The left has four isolated parcels; the right connects them with five corridors, supporting more movement and flow at the same area.
Network economics and spatial externalities gives you a practical way to ask: Which connected places or actors create more-than-additive system value? Start with the familiar case below, identify the condition that drives the result, and then test that condition in the linked exercise. Connecting upstream forest and wetland can reduce downstream flood loss more than isolated parcels.
Connecting upstream forest and wetland can reduce downstream flood loss more than isolated parcels.
“From the field, I would begin by asking: Which connected places or actors create more-than-additive system value?”
“The theoretical core is this: Nature benefits propagate through connectivity rather than adding parcel by parcel.”
“For finance and contracts, turn this into an explicit decision rule: Losing a central parcel can damage the network far beyond its area. The case shows why: Connecting upstream forest and wetland can reduce downstream flood loss more than isolated parcels.”
“But whose knowledge and whose future are missing when we frame the problem only as “Which connected places or actors create more-than-additive system value?”? Keep room to revise the institution.”
At the destination, continue to Compare counterfactuals, compare Forest focus with Integrated watershed restoration connecting forest, riparian area, and wetland, choose after checking the safety floor and maximum regret, and explain the cross-indicator effect of connectivity.
Predict the result before opening the link; after operating the control, explain the difference in one sentence.Test connectivity in the watershed game
COMMONSDo boundaries, monitoring, graduated sanctions, grievance, and rule-change rights work?
This work shows that shared resources need not collapse when users build fit-for-context institutions.
Derived design principles for durable self-governance from forests, fisheries, and irrigation systems worldwide.
Developed polycentric governance: several decision centres such as national, local, and community bodies coordinate rather than relying on one authority.
Rₜ₊₁ = Rₜ + growth(Rₜ) − Σᵢ harvestᵢ,ₜIn plain words: Next resource stock = current stock + regeneration − total extractionRₜ / Rₜ₊₁growth(Rₜ)Σᵢ harvestᵢ,ₜ88, 52, 64, and 79 are a hypothetical 0–100 resource index showing the sequence, not observed data.
Monitoring, graduated responses, and rule-change rights can return a depleted stock to recovery.
Institutional design for the commons gives you a practical way to ask: Do boundaries, monitoring, graduated sanctions, grievance, and rule-change rights work? Start with the familiar case below, identify the condition that drives the result, and then test that condition in the linked exercise. A coastal fishery lets communities revise harvest rules and handle violations proportionately.
A coastal fishery lets communities revise harvest rules and handle violations proportionately.
“From the field, I would begin by asking: Do boundaries, monitoring, graduated sanctions, grievance, and rule-change rights work?”
“The theoretical core is this: Do not reduce commons cooperation to one price or fine; inspect the institutional bundle.”
“For finance and contracts, turn this into an explicit decision rule: Rule-change rights for affected people support legitimacy and adaptation. The case shows why: A coastal fishery lets communities revise harvest rules and handle violations proportionately.”
“But whose knowledge and whose future are missing when we frame the problem only as “Do boundaries, monitoring, graduated sanctions, grievance, and rule-change rights work?”? Keep room to revise the institution.”
At the destination, finish the story, compare the three ways to launch the nitrogen market, choose the rule fixing the lake's P90 load before trading, choose confidence, and confirm the decision to see why the commons boundary precedes price.
Predict the result before opening the link; after operating the control, explain the difference in one sentence.Open the nitrogen-trading game
DYNAMIC CONTRACTHow can actors who cannot wait join the same contract as patient capital?
These researchers treat contracts as relationships in which information, effort, and payment timing evolve.
Showed how to link observable outcomes to pay so effort continues when the effort itself cannot be seen.
Showed how promised future benefit can be used to solve a long contract with hidden effort one stage at a time, a recursive dynamic-contract approach.
Analyzed contracts that update rewards and promises continuously as performance is gradually observed.
PVᵢ = Σₜ βᵢᵗ pₜIn plain words: Value to actor i = each payment discounted by that actor's patience parameter βPVᵢpₜβᵢt30, 40, and 30 are hypothetical payment shares (%) of a ¥10m example, not real contract terms.
¥3m upfront enables participation, ¥4m supports milestones, and ¥3m retained to year five rewards persistence.
Contract theory with heterogeneous discounting gives you a practical way to ask: How can actors who cannot wait join the same contract as patient capital? Start with the familiar case below, identify the condition that drives the result, and then test that condition in the linked exercise. Fund farmers’ current costs upfront while retaining payment tied to long-term soil outcomes.
Fund farmers’ current costs upfront while retaining payment tied to long-term soil outcomes.
“From the field, I would begin by asking: How can actors who cannot wait join the same contract as patient capital?”
“The theoretical core is this: The same total value creates different participation and effort when payment timing changes.”
“For finance and contracts, turn this into an explicit decision rule: Separate actor discount rates, liquidity constraints, and outcome persistence. The case shows why: Fund farmers’ current costs upfront while retaining payment tied to long-term soil outcomes.”
“But whose knowledge and whose future are missing when we frame the problem only as “How can actors who cannot wait join the same contract as patient capital?”? Keep room to revise the institution.”
At the destination, continue to the contract decision, compare success-only payment with upfront finance plus independent MRV and outcome payment, choose the design that lets the field team start while retaining outcome accountability, and read the result.
Predict the result before opening the link; after operating the control, explain the difference in one sentence.Test payment timing in the outcome-bond game
RISK TRANSFERWho ultimately carries gaps among actual loss, trigger, and claims capacity?
These researchers shaped insurance under uncertainty and the role of asymmetric information in insurance markets.
Formalized why pooling losses that one person cannot bear creates value through insurance and risk sharing.
Showed why insurance may need separating contracts for high- and low-risk buyers when only buyers know their own risk.
Premium ≈ E[Loss] + expenses + risk loadIn plain words: Premium combines expected loss, operating expense, and a loading for uncertaintyPremiumE[Loss]expensesrisk load20, 60, and 20 split a hypothetical 100-unit loss; they are not real premium or coverage rates.
Insurance does not erase loss; it reallocates layers across the insured, insurer, and shared reserve.
Insurance economics gives you a practical way to ask: Who ultimately carries gaps among actual loss, trigger, and claims capacity? Start with the familiar case below, identify the condition that drives the result, and then test that condition in the linked exercise. A reef suffers major loss but the wind trigger misses, leaving the community uncovered.
A reef suffers major loss but the wind trigger misses, leaving the community uncovered.
“From the field, I would begin by asking: Who ultimately carries gaps among actual loss, trigger, and claims capacity?”
“The theoretical core is this: Insurance transfers loss across time and actors; it does not remove it.”
“For finance and contracts, turn this into an explicit decision rule: Read basis risk, retention, prevention incentives, and solvency together. The case shows why: A reef suffers major loss but the wind trigger misses, leaving the community uncovered.”
“But whose knowledge and whose future are missing when we frame the problem only as “Who ultimately carries gaps among actual loss, trigger, and claims capacity?”? Keep room to revise the institution.”
At the destination, finish the story, compare the three reef-insurance designs, choose pre-agreed trigger cover plus a peacetime fund and trained response, select confidence, and confirm the decision to inspect payout speed, basis risk, and prevention together.
Predict the result before opening the link; after operating the control, explain the difference in one sentence.Open the reef-insurance game
HARD BOUNDSWere ecological and rights conditions that money cannot offset protected first?
These scholars shaped ecological economics around a central limit: money and equipment cannot always replace lost natural capital.
Developed the view that the economy is nested within ecological limits.
Distinguished cases where lost nature might be replaced by money or equipment from cases where it must not be replaced: weak versus strong sustainability.
Connected ecology and economics to study how strongly economic activity depends on natural functions such as water, soil, and climate.
Choose max V(x) subject to Kᴺ(x) ≥ KᴺminIn plain words: Maximize value only among plans that keep natural capital above its critical floorxV(x)Kᴺ(x)Kᴺmin45, 72, 88, and floor 60 are a hypothetical 0–100 ecological index for explanation, not observed data.
Plan A is rejected despite higher profit because ecology 45 breaches the floor 60; compare value only among B and C.
Strong sustainability and limited substitutability gives you a practical way to ask: Were ecological and rights conditions that money cannot offset protected first? Start with the familiar case below, identify the condition that drives the result, and then test that condition in the linked exercise. Exclude the most profitable coastal design when it breaches reef or consent floors.
Exclude the most profitable coastal design when it breaches reef or consent floors.
“From the field, I would begin by asking: Were ecological and rights conditions that money cannot offset protected first?”
“The theoretical core is this: Keep non-substitutable ecology and rights as constraints rather than pricing everything.”
“For finance and contracts, turn this into an explicit decision rule: Filter by hard floors first; compare remaining benefits only afterward. The case shows why: Exclude the most profitable coastal design when it breaches reef or consent floors.”
“But whose knowledge and whose future are missing when we frame the problem only as “Were ecological and rights conditions that money cannot offset protected first?”? Keep room to revise the institution.”
At the destination, first choose the ecological boundary that money cannot offset. Continue to the contract decision, choose mitigation hierarchy, registered units, and a 30-year stewardship fund as one permission condition, and inspect how non-substitutable nature is protected before trade.
Predict the result before opening the link; after operating the control, explain the difference in one sentence.Open the biodiversity-gain game
INSTITUTIONAL EVOLUTIONHow do success, betrayal, and shocks change the next strategy?
These researchers formalized how successful strategies spread through repeated adaptation rather than one-shot optimization.
Defined an evolutionarily stable strategy: one that is not easily displaced when others switch behaviour.
Showed how cooperation can grow in repeated encounters when people respond to what others did before.
Systematized game-theoretic learning through experimentation, imitation, and past performance.
ẋᵢ = xᵢ(πᵢ − π̄)In plain words: Change in strategy share = current share × (its payoff − population average)xᵢẋᵢπᵢπ̄30%, 45%, 68%, and 82% are hypothetical shares illustrating the mechanism, not experimental results.
When cooperation outperforms the average, its share can rise from 30% to 82%; profitable defection reverses the path.
Evolutionary games and institutional learning gives you a practical way to ask: How do success, betrayal, and shocks change the next strategy? Start with the familiar case below, identify the condition that drives the result, and then test that condition in the linked exercise. Delayed forest payments reduce next-period community labour and trust, weakening the coalition.
Delayed forest payments reduce next-period community labour and trust, weakening the coalition.
“From the field, I would begin by asking: How do success, betrayal, and shocks change the next strategy?”
“The theoretical core is this: Actors update strategies from payoff, trust, and observed behaviour rather than responding mechanically.”
“For finance and contracts, turn this into an explicit decision rule: The same rule can lead to different cooperation, hedging, or exit paths because history differs. The case shows why: Delayed forest payments reduce next-period community labour and trust, weakening the coalition.”
“But whose knowledge and whose future are missing when we frame the problem only as “How do success, betrayal, and shocks change the next strategy?”? Keep room to revise the institution.”
At the destination, keep one player role, lower Local jobs or Reserve for one of the six rounds, commit that round, and restore the amount in the next. Use the history to inspect the later path of trust, forest integrity, and payments.
Predict the result before opening the link; after operating the control, explain the difference in one sentence.Test learning in the forest 20-year gameTRY IT
Experience a counterfactual in Games, then transfer it to your case in Workspace. Do not stop at naming a theory—state the decision it changes.
Replay the same game with one changed condition. Compare whose payoff, ecological state, and feasibility moved.
Choose a gameChoose a model, edit safeguards, actors, evidence, or finance, and see whether the theoretical prediction appears in the decision state.
Choose a practice modelNumbers, forecasts, and scenarios are for learning. Real investment, underwriting, or legal decisions require renewed checks of source, rights, observation date, and model authorization.
02 · NOTE ARTICLES
Connect six articles to the question you want to explore.
03 · GLOSSARY
Check the words used in the games and Workspace in short, plain-language definitions.
A benefit shared by many people even when it is difficult to charge only those who use it, such as clean water or lower flood risk.
A benefit or cost that one actor's decision creates for people outside the transaction.
A design in which telling the truth and taking the promised action is also in each participant's own interest.
The condition that every actor is at least as well off by participating as by staying out.
The right to make the final decision when an event was not specified in the contract.
The set of allocations where no subgroup can leave, form its own coalition, and make itself better off.
A method that allocates value by averaging how much each actor adds across possible joining orders.
The idea that people cannot examine everything because gathering and understanding information consumes scarce time and money.
Uncertainty not only about outcomes, but also about whether the probability model itself is trustworthy.
The value of starting small, learning, and retaining the choice to expand later instead of committing all at once.
A more-than-additive effect created when places such as forests, rivers, and wetlands are connected.
A resource shared by a community or multiple users that needs rules for use and care.
The risk that actual loss and the trigger used for payment do not match, leaving needed loss uncovered.
The idea that more money or another benefit cannot fully replace some ecological conditions or rights.
The weight used to translate future benefits or payments into present value; actors differ in how long they can wait.
The system for measuring and reporting outcomes so that they can be verified.
Changing future rules or cooperative behaviour in response to success, failure, betrayal, or shocks.
Understand through four perspectives
“From the field, I would begin by asking: How does cooperation today change tomorrow’s ecology and payoff to cooperate?”
“The theoretical core is this: Treat ecological condition as a state variable changed by contributions and degradation.”
“For finance and contracts, turn this into an explicit decision rule: Early action can reduce both future loss and the cost of sustaining cooperation. The case shows why: A one-period forest-restoration delay raises fire loss and can make a downstream beneficiary’s next contribution irrational.”
“But whose knowledge and whose future are missing when we frame the problem only as “How does cooperation today change tomorrow’s ecology and payoff to cooperate?”? Keep room to revise the institution.”