Decentralized Sustainability

Beyond the Tragedy of the Commons with Smart Contracts + AI

David Dao
David Dao
Jun 15, 2018 · 11 min read

Can we scale human economic cooperation with trustable machines?

AI smart contracts leverage this new kind of programming. They are machine learning algorithms with blockchain-based business logic — or in other words, an analytical machine that can guide human behavior via designed incentives.

In this blog post, we argue that these incentive programs are highly scalable and might even provide a design-principled solution to one of the biggest environmental problems in human society: The Tragedy of the Commons.

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The Tragedy

“Therein is the tragedy. Each man is locked into a system that compels him to increase his herd without limit — in a world that is limited. Ruin is the destination toward which all men rush, each pursuing his own best interest in a society that believes in the freedom of the commons” (Hardin 1968, p. 1,244)

Hardin explains in his, now famous, article The Tragedy of the Commons that men, acting in their own self-interest, inevitably will deplete shared resources because they gain a substantial benefit in the short-term while negative consequences are shared among the whole group. The only solution according to Hardin? Centralized planning: Strong governmental interference such as privatization and regulation.

A Way Out: Sustainable Self-Governance

Nepali villagers have been self-governing their natural common resources for centuries. Can we engineer machines that guide us to become like them?

Nonetheless, human nature is (hopefully) not as grim as Hardin paints it. There are many inspiring examples of human cooperation that prevents the Tragedy of the Commons to arise. Villagers in Switzerland, have managed their communal high alpine forests since the 12th century while local communities in Nepal successfully self-governed natural water irrigation for centuries. Many commons have flourished for hundreds of years, even in periods of drought or crisis. Especially in the case of Nepal, researchers observed that self-governing communities were much more efficient than large top-down efforts from big institutions such as the World Bank or the government.

So how come some communities are able to sustainably self-govern themselves while other regions of the world experience reckless self-destruction of their common-pool resources? And, more importantly, how can we make the latter regions sustainable?

Eight Design Principles for Managing the Commons

Elinor (Lin) Ostrom was a remarkable political scientist and the first woman to win a Nobel prize in Economics. Her eight design principles are considered to be a blueprint for sustainable cooperation.

It is largely thanks to Elinor Ostrom’s decade-long extensive work in the field (which resulted in a Nobel prize in Economics!) that we know of eight robust design principles for how commons can be governed sustainably and equitably in a group:

Ostrom’s design principles (DPs) have been proven to be robust indicators for a sustainable group-based governance of shared resources, largely because they make it easier for all participating parties to trust each other. On the other side, they also show us why many regions fail to govern themselves:

  • The group is not flexible enough in its decision making to incorporate changing circumstances (DP 2, 3)
  • No efficient monitoring of social and environmental behavior (DP 4)
  • No fast access to a cheap judicial instance in case of a dispute (DP 6)
  • Outside authorities interfere and undermine the group’s rule-making rights (DP 7)

Ostrom’s principles can give us great insights into why economic cooperation can succeed or fail. What if we can compile Ostrom’s principles into a software product? Basically an economy-to-go for all the regions where Ostrom’s principles are naturally missing? Would this “deliverable economy” then indeed enable cooperation and sustainable self-governance?

Astonishingly, it is possible to translate Ostrom’s groundbreaking ideas into digital equivalences — thanks to smart contracts.

Ostrom Contracts: Blockchain + AI Systems for Sustainable Governance

Designing a system to optimize sustainable group coordination can be seen as designing incentives for a market that optimizes a sustainable objective.

Inspired by Ostrom’s work on governing the commons, we propose a new class of AI-powered smart contracts which we formalize as “Ostrom contracts”. Ostrom contracts are token-based smart contracts coupled with intelligent environmental monitoring.

Ostrom’s design principles can be translated into the smart contract design space

Blockchain-based Mechanism Design

Let’s see why this particular class of smart contracts indeed implement (or at least approximate) Ostrom’s design principles:

Token-based Membership Model (DP 1)

Decision Making with Blockchain Governance … (DP 2, 3)

… via Quadratic (Coin Lock) Voting

… via Prediction Markets

Deforestation and forest degradation account for approximately 17 percent of carbon emissions, more than the entire global transportation sector and second only to the energy sector

For example, let’s say we want to preserve the maximum possible number of 🌲 within the community. We have two candidates (A and B), who are perfect for that job. How should we decide who of them should lead our conservation efforts? One way is to let people bet money on the number of 🌲 at time X (e.g. in one year). Prediction markets have been shown to have great predictive power.

A futarchy, predicting the number of🌲at time X if we either hire A or B to lead our conservation efforts. Image credits to Gnosis (I just added a bunch of trees)!

We create two markets M1 and M2 where people can buy A or B tokens. After a while, we decide for the candidate which has the highest expected 🌲 rate (which will be equal to the token price) and refund all tokens of the other candidate. At time X, we then measure the real amount of 🌲 and pay out all token holders.

Market incentives are aligned with sustainable values in this system. We rely on the wisdom of the markets and only people who are extremely well informed on a topic will bet on it — otherwise, they are likely to lose money to others who are better informed.

As you also see, a futarchy is hard to scale if the value you want to sustain is difficult to measure (especially if it relies on trusted third parties). Fortunately, this is, where trusted intelligent machines can truly make a difference!

Increasing Stakes and Smart Contract Judge (DP 5, 6)

In case of a smart contract: If a rule violation is detected (e.g through monitoring) — we can invoke a self-enforcing contract that subtracts a fee from a deposit of a certain player. The sanction can either be invoked after voting or automatically from the sensor.

For more complicated disputes, a smart contract can also be used as a judge that is invoked or a challenge/response game can be implemented, where one group of actors is given the opportunity to submit evidence that fact X is false, and if no one submits evidence within some period of time, then X is assumed to be true.

Programmable Censorship Resistance and Complexity (DP 7, 8)

Intelligent Machine Monitoring & Learning

However, think about the most inaccessible Amazon rainforest or the vast landscape of the African Sahel region. It is impossible for a group of humans to constantly monitor such a large amount of territory. This is where scalable automation can be of great potential. In the following, we showcase a number of potential research projects in wildlife monitoring, patrol planning and prediction with great promise.

Deforestation: Scaling Environmental Monitoring for Static Resources

GainForest won the UN’s Hack4Climate competition and research on GainForest won a Microsoft AI for Earth grant.

Satellite images allow us to train classification and prediction models to evaluate and forecast rainforest deforestation. Image from GainForest.

Wildlife Conservation: Scaling Environmental Monitoring for Dynamic Resources

(Un)manned aerial vehicle can be used to efficiently monitor wildlife density. Image credits: the Elephant Atlas Project.

Another way to scale monitoring is to improve the efficiency of human patrols with intelligent suggestions. Instead of static patrol routes, Fei Fang’s lab at CMU developed game theoretical suggestions, that models the behavior of illegal poaching and thus maximizes the probability (given that model) to find traps and catch poachers.

Tightly Coupled vs. Loosely Coupled Ostrom Contracts

Tightly coupled Ostrom contracts are a class of AI smart contracts. Thus the smart contracts are responding to data flows originating from various data sources (here via intelligent monitoring) without any human in the loop.

Tightly coupled contracts go one step further and enforce execution. Everything would be automatic and there would be no human in the loop anymore. We think that full automation is extremely promising as it completely ensuring trust in the system (removing the need for all trusted third parties). However, such a system can have very dangerous side effects as the incoming data flow and AI (Oracle) have been shown to be vulnerable to biases and adversarial attacks.


Surely there are many practical challenges ahead: How can we guarantee that every player has the ability to connect to the internet/blockchain? How are tokens paid out in fiat coins? Are there any security concerns or ways an adversary could attack and game the contracts?

However, there is a lot of groundbreaking potential.

First of all, it is possible to implement complex political and economic ideas into code. Something which wasn’t imaginable ten years ago. Smart contracts allow us to execute computer functions while digitally sending money at the same time. This is powerful as it means that we can implement and possibly install economic systems anywhere and everywhere, just like a computer program.

Second, exactly this ability to quickly implement and try out different systems also allow us to run experiments and use scientific methods (empirical cryptoeconomics) to evaluate, reiterate and possibly improving upon different variations of economies.

Finally, the dawn of AI in many of our daily applications means that we can (and will) also use intelligent agents to augment smart contracts in the future. Combining intelligence with incentives promises to align human self-interest with social good. Ostrom contracts are just one idea how this can be used for the benefit of society.

If you are interested in this research topic, please don’t hesitate to contact me and the DS3Lab at ETH Zurich.

If you want to support us in starting a pilot project with, send us a mail at


David Dao

Written by

David Dao

AI Researcher ETH Zurich || Stanford || Berkeley || MIT Broad ||