Mechanism design theory is a branch of economics and game theory born in the 1960s. Where standard economics generally tries to predict the outcome of certain fixed policies, mechanism design does the reverse. The outcome is fixed and the designer’s challenge is to create a mechanism, which can be a game or an algorithm, that will yield that exact outcome. In our case, we are trying to find the mechanism that maximizes human well-being within our planet’s boundaries.

Mechanism design can be seen as the engineering part of economics or as a reverse form of game theory. In trying to elicit a certain kind of behavior from the participants, the designer’s task is to design a specific game that makes the participants behave in the desired way. Like game theory, mechanism design involves formal and often played game structures that illuminate how human choices are shaped by the pressures exerted on them. For mechanism design, the choice is a given and the specific pressure is what needs to be created.

Mechanism design plays a vital part in drafting treaties, regulations, institutions and financial incentives. It is particularly famous for creating innovative forms of auctions that yield better outcomes.

A classic conundrum is how best to auction off public property so that the taxpayer gets the best price for the asset and that the asset ends up in the right hands. Let’s say a national government is selling part of their radio spectrum and three cell phone companies are bidding for it. All three companies do an internal evaluation of the spectrum and given their current technology and subscriber bases, Company A values the asset at $50 million, Company B at $55 million and Company C at $60 million.

In a traditional auction the winning strategy is to bid as little as possible without losing to a competitor, because offering the full value and winning would leave no room for profit. Here the public is unlikely to receive the full value of the asset and the winner could be chosen somewhat arbitrarily, not based on who can put the spectrum to best use.

The task of mechanism design is to have an end result in mind and work backwards from there. In 1961 Columbia professor William Vickrey proposed to solve the public auction conundrum with a mechanism called the second-price sealed-bid auction. Here the bidders submit sealed bids without knowing what their competitors have bid. The highest bidder wins, but they have to pay only the second highest bid amount.

Now it becomes every bidder’s incentive to bid the true value, since even when they win, they don’t have to pay it. This way the public leaves as little money on the table as possible and the asset ends up with the company that most benefits from it, in this case Company C.

To express its ideas, mechanism design doesn’t resort to written words, but is able to reduce its games to mathematical equations that can be solved like calculations. Mechanism design can be best applied to cases isolated from their larger environment, like auctions and theoretical models, because they are unencumbered by pre-existing legislation or legacy mechanisms that would hamper the design. In the real world, designers can rarely start from a tabula rasa position, as they have to take into account all the other mechanisms that are already in play.

As we create new models with the help of systems theory and mechanism design theory, we can study their viability with computer simulations. Agent-based modeling could be especially helpful in simulating the cumulative impact the actions and interactions of individual citizens can have on society under different sets of rules. In agent-based modeling every agent can act autonomously based on the parameters of the simulation.

The agent can be, for example, a tree in a fire-prone forest, and its proximity to a burning tree will determine the likelihood that it, too, will catch fire and spread the fire to the trees around it. These simulations are especially adept at modeling emergent phenomena, where the actions of individuals add up to something much bigger than the sum of their parts. Simulations are only models, but they can still provide valuable insights into the problems we are trying to solve, particularly when it comes to revealing the flawed assumptions inherent in the models we have built.

To be clear–computers are unable to adequately simulate the individual psychology or behavior of an individual. But in large numbers, as the Law of Large Numbers suggests, human behavior can become quite predictable. When large numbers of individuals are studied at the same time, most human behavior will start to follow the standard deviation of a bell curve. This increases the predictive power of agent-based modeling.

In our simulations the agents would represent average citizens living under a new financial and political system. Their needs, on average, would be knowable as would the overall resources at their disposal. We can then propose various kinds of mechanisms and systems and run numerous simulations based on them, making tweaks and changes along the way. By adding data and refining the model itself, we can produce better and better simulations. Positive results can eventually lead to small-scale trials in real life. The theoretical and practical advice systems theory and mechanism design provide us is invaluable. Neither science relies on words to build their systems and their mechanisms. One relies on maps and charts and the other relies on math. If words are not precise enough for their models, we must ask: Are they precise enough for our solution?

While I lack the mathematical skills to turn the solution I propose into math, eventually the principles will have to be translated into this universal language. This implies that in the future, our constitution wouldn’t be written on parchment or paper, but that we would adopt an electronic constitution written in computer code and expressed as mathematical equations.