In this paper, we investigate the dichotomy between system design delegation driven by requirement allocation and delegation driven by objective allocation. Specifically, we investigate this dichotomy through the lens of agency theory, which addresses cases where an agent makes decisions on behalf of another, that is, a principal. In current practice, design delegation largely involves requirement allocation as a means to inform agents of the desirable system characteristics. The value‐driven design paradigm proposes replacing requirements with objective, or trade‐off, functions to better guide agents toward optimal systems. We apply and adapt the principal–agent mathematical model to the design delegation problem to determine if a principal, that is, the delegator, should communicate using requirements or objectives with her agent. In this model, we assume the case of a single principal and single agent where the agent has certain domain knowledge the principal does not have and the agent accrues costs while solving a delegated design problem. Under the assumptions of the mathematical model, we show that the requirement allocation paradigm can yield greater value to the principal over objective despite limitations requirement allocation places on the principal to learn information from the agent. However, relaxing model assumptions can impact the value proposition of requirement allocation in favor of objective allocation. Therefore, a resolution to the requirement–objective dichotomy may be context dependent. The results and the analytical framework used to derive them provide a new, foundational perspective with which to investigate allocation strategies.
COVID-19 Brings Data Equity Challenges to the Fore
The COVID-19 pandemic is compelling us to make crucial data-driven decisions quickly, bringing together diverse and unreliable sources of information without the usual quality control mechanisms we may employ. These decisions are consequential at multiple levels: They can inform local, state, and national government policy, be used to schedule access to physical resources such as elevators and workspaces within an organization, and inform contact tracing and quarantine actions for individuals. In all these cases, significant inequities are likely to arise and to be propagated and reinforced by data-driven decision systems. In this article, we propose a framework, called FIDES, for surfacing and reasoning about data equity in these systems.
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- NSF-PAR ID:
- 10287320
- Date Published:
- Journal Name:
- Digital Government: Research and Practice
- Volume:
- 2
- Issue:
- 2
- ISSN:
- 2691-199X
- Page Range / eLocation ID:
- 1 to 7
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Merging multiple data streams together can improve the overall length of record and achieve the number of observations required for robust statistical analysis. We merge complementary information from different data streams with a regression-based approach to estimate the 1 April snow water equivalent (SWE) volume over Sierra Nevada, USA. We more than double the length of available data-driven SWE volume records by leveraging
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3440889
