In the context of non-profits we find ourselves in a peculiar modeling setting for traditional game theory scenarios. Instead of basing interactions on a monetary basis, one is interested in maximizing welfare among beneficiaries. In this blog post I introduce a new game scenario that intends to address thi scenario.
Throughout this blog post, large non-profits are interchangeably referred to as “central agents” and smaller subsidiaries as “agents”. Smaller subsidiaries could mean people being directly benefited by the goods distributed by the central agent, sub-projects within the central agent organization or local non-profits responsible for passing the goods further on to beneficiaries.
Challenges
Resource allocation in the non-profit context might be challenging because agents are not necessarily motivated to act truthfully. A few different issues might arise:
a) even though all agents might be trying to effectively employ the best use of their resources to optimize for welfare, these efforts might not be aligned with the central agent’s goals;
b) agents might have access to private information about local demand that won’t be shared with the central agent;
c) the agents are at least somewhat more motivated to help their own clients than those of other agents Lundy et al. [1] and/or;
d) agents might simply be corrupt and act only with their own interest in mind.
Approach to Addressing Challenges
This blog post introduces the idea of charitable games. It is defined by the fact that there’s no flow of goods nor any sort of monetary transaction from agents to central agents and agents are incentivized to act truthfully to their claims. This algorithm is being developed in order to help non-profits on their resource prioritization.
Given the success of auction games for incentivizing truthful report of agents, Chakravarty and Kaplan [2] and Hartline and Roughgarden [3], I propose the use of a modified first-price sealed-bid (FPSB) multi-agent auction. The traditional FPSB multi-agent auction algorithm has each agent submitting a bid in secret without knowing the bids of the other agents. The resource is awarded to the highest bidder, and the price paid is the amount of the highest bid.
To address the financial inconsistencies in charitable game scenarios, the model (which will be further explained in the following posts) aims to use the agents' inputs to calculate their utility. This calculation is based on a function determined by a central agent and a reinforcement learning environment.
In a nutshell, this approach can be divided into two parts: Firstly, the central agent receives self-declared data from the agents. Using this data, the central agent can calculate the utility function that ranks the agents from those with the highest utility output to those representing the lowest utility values.
As for the second part of the model, it involves the act of simulating a credible environment in which the agents will be put into test. Each agent will have its own environment and will start off with their self-declared variables. These variables will change over time as the reinforcement learning algorithms aim to optimize for the utility function specified by the central agent. The result of running this scenario will offer us different values of the imputed variables and a new value for the output of the utility function.
Now the self-regulating function of an auction model's truthfulness comes into play. We compare both the variables provided by the agents and the final variables obtained from running the reinforcement learning scenarios. If the difference between these two sets of variables is small, it means the agents were honest and competent—they accurately predicted the impact they would have in a given scenario. If the difference is large, it suggests the agents were either dishonest or failed to access how impactful they could be given a number of resources.
There are many ways to interpret the results given by this model. For example, one might be more interested in choosing among the agents that better assessed their impact rather than who had the highest utility function. I intend to expand on these considerations further in furthering this work.
The advantages of this model consists in running impossible scenarios. In the real world the resources allocated to charities is very limited and they constantly have to make difficult decisions in how to allocate these resources. It’s hardly ever possible to run experiments to figure out what’s the best way of distributing these resources because it’s so costly to do so. This approach has some advantages inherited from it’s highly analytical profile. The fact that the exact same environment will be used for all competing agents adds a degree of fairness to the process, allowing them to compete on an equal footing. It also makes it easy to test the same agents through many different kinds of scenarios. This model's purpose is to bridge the gap between policy makers or people allocating resources and their future impacts.
However, the disadvantages of this approach are manifold. No model is able to predict accurately diverse and complex human settings like the ones in which non-profit act. The data and models are incomplete, and the individuals often primarily responsible for building them have a very narrow perspective of the scenarios and realities they're modeling. Therefore, the aim of this work is not to serve as an oracle offering a clear vision of the future, but rather as another information source for those making extremely difficult decisions.
References
[1] Taylor Lundy, Alexander Wei, Hu Fu, Scott Duke Kominers, and Kevin Leyton-Brown. 2019. Allocation for Social Good: Auditing Mechanisms for Utility Maximization. In ACM EC ’19: ACM Conference on Economics and Computation (EC ’19), June 24–28, 2019, Phoenix, AZ, USA. ACM, New York, NY, USA, 19 pages. https://doi.org/10.1145/3328526.3329623
[2] Surajeet Chakravarty and Todd Kaplan. 2013. Optimal allocation without transfer payments. Games and Economic Behavior 77, 1 (2013), 1–20.
[3] Piotr Dworczak and ® Scott Duke Kominers ® Mohammad Akbarpour. 2019. Redistribution through markets. (2019). Becker Friedman Institute Working Paper.
I collected some input from Beneficiaries which would be agents in your case :)
1.) Most business donators Drive their decisions by business opportunities, so they weight a support more for agents which offer them that
2.) Most larger Donation distributors pay out financial aid
Either as a lease without interest to be returned after a certain time
Or a share of the profit ( In case of hospitals)
3.) Agent Reports nowadays always need to include business and cost plans and how the region will provide in terms of economics.
For 3.) I can provide samples of agent Reports regarding a hospital in Kenya :)
I really appreciate this post, I think it's a great approach to use tech to help the world. So many people don't seem to care whether their research actually applies to the real world, but this post demonstrates recognition of the importance of having the conversation between the real-world applications and development of the analysis tools themselves.