51 research outputs found
On the Aggregation of Subjective Inputs from Multiple Sources
When we have a population of individuals or artificially intelligent agents possessing diverse subjective inputs (e.g. predictions, opinions, etc.) about a common topic, how should we collect and combine them into a single judgment or estimate? This has long been a fundamental question across disciplines that concern themselves with forecasting and decision-making, and has attracted the attention of computer scientists particularly on account of the proliferation of online platforms for electronic commerce and the harnessing of collective intelligence. In this dissertation, I study this problem through the lens of computational social science in three main parts: (1) Incentives in information aggregation: In this segment, I analyze mechanisms for the elicitation and combination of private information from strategic participants, particularly crowdsourced forecasting tools called prediction markets. I show that (a) when a prediction market implemented with a widely used family of algorithms called market scoring rules (MSRs) interacts with myopic risk-averse traders, the price process behaves like an opinion pool, a classical family of belief combination rules, and (b) in an MSR-based game-theoretic model of prediction markets where participants can influence the predicted outcome but some of them have a non-zero probability of being non-strategic, the equilibrium is one of two types, depending on this probability -- either collusive and uninformative or partially revealing; (2) Aggregation with non-strategic agents: In this part, I am agnostic to incentive issues, and focus on algorithms that uncover the ground truth from a sequence of noisy versions. In particular, I present the design and analysis of an approximately Bayesian algorithm for learning a real-valued target given access only to censored Gaussian signals, that performs asymptotically almost as well as if we had uncensored signals; (3) Market
making in practice: This component, although tied to the two previous themes, deals more directly with practical aspects of aggregation mechanisms. Here, I develop an adaptation of an MSR to a nancial market setting called a continuous double auction, and document its
experimental evaluation in a simulated market ecosystem
Near-Optimal Target Learning With Stochastic Binary Signals
We study learning in a noisy bisection model: specifically, Bayesian
algorithms to learn a target value V given access only to noisy realizations of
whether V is less than or greater than a threshold theta. At step t = 0, 1, 2,
..., the learner sets threshold theta t and observes a noisy realization of
sign(V - theta t). After T steps, the goal is to output an estimate V^ which is
within an eta-tolerance of V . This problem has been studied, predominantly in
environments with a fixed error probability q < 1/2 for the noisy realization
of sign(V - theta t). In practice, it is often the case that q can approach
1/2, especially as theta -> V, and there is little known when this happens. We
give a pseudo-Bayesian algorithm which provably converges to V. When the true
prior matches our algorithm's Gaussian prior, we show near-optimal expected
performance. Our methods extend to the general multiple-threshold setting where
the observation noisily indicates which of k >= 2 regions V belongs to
A Study of the Grunwald-Letnikov Definition for Minimizing the Effects of Random Noise on Fractional Order Differential Equations
Of the many definitions for fractional order differintegral, the
Grunwald-Letnikov definition is arguably the most important one. The necessity
of this definition for the description and analysis of fractional order systems
cannot be overstated. Unfortunately, the Fractional Order Differential Equation
(FODE) describing such a systems, in its original form, highly sensitive to the
effects of random noise components inevitable in a natural environment. Thus
direct application of the definition in a real-life problem can yield erroneous
results. In this article, we perform an in-depth mathematical analysis the
Grunwald-Letnikov definition in depth and, as far as we know, we are the first
to do so. Based on our analysis, we present a transformation scheme which will
allow us to accurately analyze generalized fractional order systems in presence
of significant quantities of random errors. Finally, by a simple experiment, we
demonstrate the high degree of robustness to noise offered by the said
transformation and thus validate our scheme.Comment: 4th IEEE International Conference on Information and Automation for
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