arXiv subjects

Sukolsak Sakshuwong

Publications and source records attributed to Sukolsak Sakshuwong.

5 recordsLinked to original sources

arXiv2026

Technology-Supported Moderation in Online Deliberation

Mini-publics are an increasingly popular family of democratic innovation to broaden citizen engagement. However, convening a representative sample in person is resource-intensive, and requires significant organizational efforts to coordinate travel, venue logistics, and facilitator training. This chapter outlines the design and development of the Stanford Online Deliberation Platform, initially tailored for Deliberative Polling, building upon insights from in-person moderation in that setting, but supporting a broader range of methods and approaches in the deliberative space. The platform supports structured conversations for small groups (ideally 8-15 people) with automated moderation functionality, enabling the groups to assume ownership of the deliberative conversation rather than relying on a moderator persona. We describe the platform's architecture and key design decisions, including the deliberation flow, room assignment, speaker management, speaking nudges, agenda management, outcome development, and real-time monitoring. Between its inception in 2018 and 2025, the platform has facilitated over 30,000 unique participants engaging in deliberations; we catalog 54 deliberations with at least 100 participants up to December 2025, spanning a diverse range of languages, countries, and topics. Finally, we establish a standardized vocabulary for future analyses by defining the terminology employed in the platform's data and documenting significant software revisions over time.

cs.CY↗

arXiv2023

Low Sample Complexity Participatory Budgeting

We study low sample complexity mechanisms in participatory budgeting (PB), where each voter votes for a preferred allocation of funds to various projects, subject to project costs and total spending constraints. We analyze the distortion that PB mechanisms introduce relative to the minimum-social-cost outcome in expectation. The Random Dictator mechanism for this problem obtains a distortion of 2. In a special case where every voter votes for exactly one project, [Fain et al '17] obtain a distortion of 4/3 We show that when PB outcomes are determined as any convex combination of the votes of two voters, the distortion is 2. When three uniformly randomly sampled votes are used, we give a PB mechanism that obtains a distortion of at most 1.66, thus breaking the barrier of 2 with the smallest possible sample complexity. We give a randomized Nash bargaining scheme where two uniformly randomly chosen voters bargain with the disagreement point as the vote of a voter chosen uniformly at random. This mechanism has a distortion of at most 1.66. We provide a lower bound of 1.38 for the distortion of this scheme. Further, we show that PB mechanisms that output a median of the votes of three voters chosen uniformly at random have a distortion of at most 1.80.

cs.GT↗

arXiv2020

Knapsack Voting for Participatory Budgeting

We address the question of aggregating the preferences of voters in the context of participatory budgeting. We scrutinize the voting method currently used in practice, underline its drawbacks, and introduce a novel scheme tailored to this setting, which we call "Knapsack Voting". We study its strategic properties - we show that it is strategy-proof under a natural model of utility (a dis-utility given by the $\ell_1$ distance between the outcome and the true preference of the voter), and "partially" strategy-proof under general additive utilities. We extend Knapsack Voting to more general settings with revenues, deficits or surpluses, and prove a similar strategy-proofness result. To further demonstrate the applicability of our scheme, we discuss its implementation on the digital voting platform that we have deployed in partnership with the local government bodies in many cities across the nation. From voting data thus collected, we present empirical evidence that Knapsack Voting works well in practice.

cs.GT↗

arXiv2019

Who is in Your Top Three? Optimizing Learning in Elections with Many Candidates

Elections and opinion polls often have many candidates, with the aim to either rank the candidates or identify a small set of winners according to voters' preferences. In practice, voters do not provide a full ranking; instead, each voter provides their favorite K candidates, potentially in ranked order. The election organizer must choose K and an aggregation rule. We provide a theoretical framework to make these choices. Each K-Approval or K-partial ranking mechanism (with a corresponding positional scoring rule) induces a learning rate for the speed at which the election correctly recovers the asymptotic outcome. Given the voter choice distribution, the election planner can thus identify the rate optimal mechanism. Earlier work in this area provides coarse order-of-magnitude guaranties which are not sufficient to make such choices. Our framework further resolves questions of when randomizing between multiple mechanisms may improve learning, for arbitrary voter noise models. Finally, we use data from 5 large participatory budgeting elections that we organized across several US cities, along with other ranking data, to demonstrate the utility of our methods. In particular, we find that historically such elections have set K too low and that picking the right mechanism can be the difference between identifying the ultimate winner with only a 80% probability or a 99.9% probability after 400 voters.

cs.GT↗

arXiv2017

Sequential Deliberation for Social Choice

In large scale collective decision making, social choice is a normative study of how one ought to design a protocol for reaching consensus. However, in instances where the underlying decision space is too large or complex for ordinal voting, standard voting methods of social choice may be impractical. How then can we design a mechanism - preferably decentralized, simple, scalable, and not requiring any special knowledge of the decision space - to reach consensus? We propose sequential deliberation as a natural solution to this problem. In this iterative method, successive pairs of agents bargain over the decision space using the previous decision as a disagreement alternative. We describe the general method and analyze the quality of its outcome when the space of preferences define a median graph. We show that sequential deliberation finds a 1.208- approximation to the optimal social cost on such graphs, coming very close to this value with only a small constant number of agents sampled from the population. We also show lower bounds on simpler classes of mechanisms to justify our design choices. We further show that sequential deliberation is ex-post Pareto efficient and has truthful reporting as an equilibrium of the induced extensive form game. We finally show that for general metric spaces, the second moment of of the distribution of social cost of the outcomes produced by sequential deliberation is also bounded.

cs.GT↗