Mechanism design for social good

Abstract

Across various domains---such as health, education, and housing---improving societal welfare involves allocating resources, setting policies, targeting interventions, and regulating activities. These solutions have an immense impact on the day-to-day lives of individuals, whether in the form of access to quality healthcare, labor market outcomes, or how votes are accounted for in a democratic society. Problems that can have an outsized impact on individuals whose opportunities have historically been limited often pose conceptual and technical challenges, requiring insights from many disciplines. Conversely, the lack of inter-disciplinary approach can leave these urgent needs unaddressed and can even exacerbate underlying socioeconomic inequalities.

Roles for computing in social change

“…Computational approaches, used in tandem with other empirical methods, can provide crucial evidentiary support for work that attends to values in technology -even if computing 1 These proposals are in the spirit of other scholars' recent calls to recognize the political nature of computational work and articulate new modes of engagement for computing (e.g., [49]). 2 While we include some examples from low-and middle-income nations, many of the examples and hence discussions are focused around the United States and other developed nations. There is an emerging set of discussions around risks and challenges in using machine learning specifically in the context of developing nations, including the annual Machine Learning for Development Workshop Series [1], which also faces many of the structural challenges we engage here.…”

“…And like any attempt to effectuate change, the approaches we outline carry hazards of their own, which we also explore below. 2…”

Acceptable Planning: Influencing Individual Behavior to Reduce Transportation Energy Expenditure of a City

“…It puts reasoning about human behavior as the center of intelligent system design. Our approach aligns with the perspective of Abebe and Goldner (2018), who argue that AI for social good inherently is an inter-disciplinary problem and developing good AI solutions requires deep grounding in the field of interest and collaboration with domain experts. To develop our AI planning-based approach, we engage with behavioral economics to understand how people choose and with transportation research to estimate energy consumption.…”

Subsidy Allocations in the Presence of Income Shocks

“…Our paper can be viewed as belonging to an emerging style of work that uses computational and optimizationbased methods to inform assistance programs aimed at improving access to opportunity for vulnerable populations (Abebe and Goldner 2018a;2018b). A recent study considers allocating interventions for homelessness services using a mixture of counter-factual reasoning and mechanism design (Kube, Das, and Fowler 2018); another studies optimal allocation of financial aid in US colleges based on students' parental income (Findeisen and Sachs 2016).…”