Legacy Default

Overview

This model is based on the current status quo of scientific funding and value flow. It consists of researchers competing for funding from an exhaustible funding agency via research proposals. The research grant is then spent on 1. research costs (e.g. equipment, data, etc.) and 2. getting the research published in a journal.

In this model, the knowledge curators (journals) lock most of the value from the research and they have full control over who gets access to the knowledge assets that have been published. This in turn means that researchers who have been given a grant in the past have a much higher chance of receiving grants in the future and while this itself has multiple parameters (e.g. expertise in the field, the ability to produce high quality proposals, reputation, etc.), it is modelled by a single variable in the simulation called knowledge_access.

A schema of the baseline model (current scientific research pipeline

Apart from knowledge curators locking value, there is a significant loss in value and time due to a lack of incentive to share research data and collaborate (note that research papers don't usually include all the data that has been collected). As a result, if the same dataset is useful for two independent research projects, it would have to be collected twice since researchers don't have incentive to share their work with each other and they also have limited access to other people's research via knowledge curators.

Parameters

In this model, each researcher generates a proposal. The proposal is itself just a Python dictionary with the following attributes:

• grant_requested: random integer in the range [10 000, 50 000]

• assets_generated: random integer in the range [1, 10], represents the expected outcome of the research and its value (e.g. number of new algorithms, amount of new data, etc.)

• no_researchers: fixed for now, but could be randomized in another experiment

• knowledge_access: starts at 1, then increments by 1 if a research proposal is accepted. It should serve as a representation of researchers gaining more knowledge by doing research and then being more able to do further research (in this simulation, it actually works like a very rudimentary reputation system). The main idea is to give an advantage to researchers who have been funded previously.

Behavior

In this model, two researchers are competing for funding that comes from an external grant-giving agency and the university acts as the agent that judges their research proposals based on predefined properties (as of right now, this includes: grant requested, the proposed outcome of the research, number of researchers working on the project, and the access of knowledge (which is indicative of whether the researchers can successfully complete the project)). Once a researcher (or a research team) wins a grant, they use the funding to publish knowledge assets to knowledge curators (e.g. scientific journals). In this model, every successful research proposal leads to an increase in knowledge access for the researchers working on the project (in simple terms, doing research increases your level of knowledge), thus increasing the likelihood of them receiving grants in the future (the access to knowledge is similar to reputation, which presumably also increases with more research projects completed).

Limitations

• fixed number of researchers, grant size, project length, and asset output size (not reflecting the real-world variability of research projects, which usually have teams of multiple people actively working for long periods of time)

• fixed price for marketplace assets (clearly, different services will have different prices, datasets may vary in size, algorithms may vary in the price for their utility)

• even distribution of funds to asset sellers (this will result in a gradual decrease in revenue/seller (since the money flowing through the marketplace is constant), which is on one hand slightly representative of the scenario where more people are selling their assets, thus increasing the competition and supply, hence lowering revenue, but on the other, it fails to represent the variability of different assets offered)

Further Reading