have been around for a few decades now. They are one of the main methodologies behind the study of . Agent-based models complex systems The main idea behind ABMs is the modelling of systems by modelling their individual parts. This idea comes in contrast to the standard mathematical modelling, which focuses on higher-level behaviours of a system. Why would someone want to focus on this? There are two main reasons: ABM can model non-linear and chaotic behaviours of systems, which are very difficult to capture with other types of models. ABM’s are very intuitive. For example, if we are modelling a market, we can model each individual trader, based on a few simple behaviours. An example of early successes in was Schelling’s segregation model. ABM demonstrated how communities can get segregated, even when the individuals are not trying to achieve segregation themselves. Thomas Schelling We assume that all people live on a square grid and that their only requirement is that half their neighbours are of the same colour. They are perfectly happy if half of their neighbours are of some other colour, so they are not racist in the sense that they demand that all their neighbours are like them. If this assumption is not met, then they seek to move elsewhere. Even under these assumptions, the model converges to a situation where distinct segregated areas are created. Agent-Based Modelling and Economics As you might have guessed, it makes absolute sense to use this type of model in economics. is usually concerned with higher-level models, things such as supply and demand curves. Economic methodology However, these models require an oversimplification of the underlying dynamics. The most notable example is the , which has been much disputed after the . assumption of rationality financial meltdown of 2008 had written up an article discussing whether ABM would have been able to predict the crisis, that traditional models failed to predict. The Economist have been around for a bit more than a decade. Agent-based computational economics One of the prime examples is the model developed by and and detailed in their book , which concerns a fictional economy where agents live on ‘sugar’. Sugarscape Joshua M. Epstein Robert Axtell Growing Artificial Societies The agents consume sugar and can reproduce, trade, transfer information etc. like simplifications of real humans. A simulation of the model is shown below. So, what does all this has to do with and ? tokenomics ICOs Tokenomics allow us to create artificial economies with artificially constructed incentives. All white papers in this area make all kinds of assumptions as to token adoption, usage, forecasts of future value, etc. However, I’ve rarely stumbled upon a convincing white paper in this area. The simple reason is that is a relatively new field. Actually, many people won’t even recognise the term. tokenomics There are no standard rules as to how to set up a token economy and what to expect in terms of forecasts and usage. Research in this area is also scarce. However, it is clear that such as a framework for evaluating and assessing token economies is needed for two reasons. desperately First, it would allow the founders of the economy to understand its pros and cons and improve their model. Secondly, it can offer ICO investors some assurance as to what to expect when investing, since such a framework could be used to forecast future value and adoption. Agent-Based Modelling and Tokenomics Therefore, with no solid theoretical framework or mathematical models of economics to rely on, how can someone build up a solid framework for their token economy? This is where becomes a natural choice for tokenomics. agent based modelling The modelling of token economies through ABM allows us to bypass any theoretical limitations and model the agents of our assumptions directly, while at the same time taking into account any kind of constraint or assumption we want. This is the model I used successfully for . Through an ABM it became possible to demonstrate that, if the assumptions behind the business model of are correct, then their token should be close to parity with the Great British Pound. Crowd for Angels’ ICO Crowd for Angels I believe the need for ABM in tokenomics becomes even more important when we consider the mistakes that many startups do when designing their token economies (something about which I wrote up in another ). post The fact that ICOs have managed to raise around should on one hand get us excited, but at the same time cautious. There is still lots of work that needs to be done in regulating ICOs, and so far, none of this work is concerned about the 3 most important questions: $5.6 billion Can a particular token economy work? How well can it work, what is the maximum possible valuation? How volatile and sensitive it is? Is it stressed-tested? However, with the use of I am confident that these questions can be studied and answered with reasonable degrees of certainty. agent-based modelling Building a good tokenomics model is not easy (as I explain in the video below). However, with the aid of things become considerably easier. It is time that we take this great tool out of our toolbox and back into the forefront! agent based modelling Co-published here.

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