Just like larger organizations, those that operate at a local level, such as the Chicago Department of Planning and Development, or the Residents’ Welfare Association, seek to use their budgets to effectively allocate taxpayer/stakeholder money towards the most pressing problems. Stakeholders are often skeptical towards the organization’s choices. In polls conducted by Gallup, about one in every three Americans does not have much confidence that local governments are adept at handling their local problems, and this number has remained roughly consistent since the 1970s. We also think that part of the problem might be attributed to voters or residents feeling isolated from the decision making and that current polls conducted in some places are limited and do not properly address the concerns of the affected stakeholders. By utilizing algorithms and swarm intelligence, a concept borrowed from the behavior of groups of bees or fish in nature, we can utilize the ‘wisdom of the crowds’ and allow stakeholders to collectively reach more efficient and effective budgeting solutions that will provide the most satisfied outcomes for each party.
We are proposing a swarm intelligence-based platform, which is defined as the collective behavior of decentralized and self-organized systems. Swarm intelligence based platforms have shown to make more accurate predictions as it provides the interfaces and algorithms to enable “human swarms” to converge online, combining the knowledge, wisdom, insights, and intuitions of diverse groups into a single emergent intelligence. (One of many examples is the prediction of the winners of the Kentucky derby.) ComPulse can expand the use cases to budgeting decisions, which are partly prediction problems (considerations of which areas require the most care) and partly judgment/opinion problems.
The technology stack consists of a user interface, commonly called the ‘design space,’ which would be intuitive in terms of the options represented. Users would make an initial choice and be able to view the rest of the swarm and their choice distribution. Another key aspect is an ‘egalitarian choice architecture,’ in order to minimize systemic bias in all aspects of the system such as question structure and sequence. ComPulse would be able to help organizations in screening and posting of questions that would be accessible to a large and diverse audience. The back-end of the stack will be powered by AI algorithms that are focused on optimization techniques and are part of a larger family of swarm-based collective decision making algorithms.
Fig. 1 – Illustrative representation of initial design space for candidate prediction (Source: Unanimous.ai)
ComPulse would start by gathering the opinions of experts from different bodies or areas whom would collect points on their contributions, which could be utilized as discounts on posting fees when they initiate a budget proposal as an incentive for them, on higher level open-ended questions. These opinions would be aggregated and split into defined choices for each question or problem where the broader crowd who are relevant to these proposals (e.g local residents) would pick their choices through a thumbs up or thumbs down or alternatively make exclusive picks.
There are a few key parts using this technology:
- Bringing together a diverse group of individuals such as administrators, residents, and development planners who are knowledgeable about the questions at hand.
- Decreases bias, increases representation of various interests, entices engagement and allows for compromise.
- For a specific question, each member makes an initial pick at roughly the same time and can see choices for the rest of the swarm (Refer to Fig. 1). Can decrease herd mentality which is often present during an ongoing poll.
- Users interact within the design space through simple push-pull mechanisms in order to reach consensus on a certain question and are allowed to change their answers (Refer to Fig. 2).
- In cases of no clear consensus, opportunity for further discussion and debate regarding problems.
Fig.2 – Illustrative representation of final consensus for candidate prediction (Source: Unanimous.ai)
So far, this technology has been used mostly in different applications from predicting sports and financial trends, to assessing the effectiveness of advertisements and movie trailers, real-time swarms have been shown to significantly amplify intelligence in each area. By combining this technology with a situation in which many diverse stakeholders are brought together to determine a solution it optimizes these solutions, reaches decisions and makes more accurate predictions on outcomes.
It is difficult to measure the tangible benefit from such improvements due to the difficulty of quantifying efficient decision making and increased voter trust. However, in order to test a potential local use case, we interviewed some members from the University of Chicago on their thoughts on the student government’s $2.3 mn annual budget that needs to be allocated amongst diverse functions and opinions to support the student life and organizations within the university. Citing a representative in the Student Government “This could be a good opportunity for us to reach out to the larger student body and make them feel adequately represented”
ComPulse could charge a posting fee to the organization for each set of questions as a result of facilitating the definition of the problem. Moreover, contracts with large organizations that have a number of local branches with different sets of problems (e.g., City governments) would be a way to reach a large number of stakeholders and become a key part of the decision-making process for a number of organizations.
ComPulse’s business model would consist of a for-profit side supporting its not-for-profit arm: profitability can be found in the B2B space, contracting within and between large businesses’ employee bases as they seek to maximize the efficiency of departmental or project-based thought processes. The side serving state and local governments would be primarily non-profit.
A platform that could be so central to augmenting decision making in organizations is not without a few risks. One that was pointed out during our interviews was pressure from incumbents who might not want to dilute the budgetary decision-making powers that they hold (Ivy Missen, UChicago Student Government). Another potential risk would be fears of the lack of adequate representation, especially for those without access to technology. ComPulse would work with organizations to ensure reduction of bias in the sampling process.
Gallup Government Trust Polls: http://news.gallup.com/poll/5392/trust-government.aspx
Unanimous Case Studies: https://unanimous.ai/case-studies/
Swarm Intelligence and Democracy: https://eanfar.org/can-swarm-intelligence-save-democracy/
Akay and Karaboga, Algorithms Simulating Bee Swarm Intelligence: https://www.researchgate.net/profile/Dervis_Karaboga/publication/220638051_Akay_B_A_Survey_Algorithms_Simulating_Bee_Swarm_Intelligence_Artificial_Intelligence_Review_31_68-85/links/5666a8c208ae4931cd627ba8/Akay-B-A-Survey-Algorithms-Simulating-Bee-Swarm-Intelligence-Artificial-Intelligence-Review-31-68-85.pdf
UChicago Student Government Budget: http://sg.uchicago.edu/budget/