Results 1 -
9 of
9
Distributed Decision Making by Categorically-Thinking Agents
- in Decision Making With Imperfect Decision Makers
"... This paper considers group decision making by imperfect agents that only know quantized prior probabilities for use in Bayesian likelihood ratio tests. Global decisions are made by information fusion of local decisions, but information sharing among agents before local decision making is forbidden. ..."
Abstract
-
Cited by 2 (2 self)
- Add to MetaCart
(Show Context)
This paper considers group decision making by imperfect agents that only know quantized prior probabilities for use in Bayesian likelihood ratio tests. Global decisions are made by information fusion of local decisions, but information sharing among agents before local decision making is forbidden. The quantization scheme of the agents is investigated so as to achieve the minimum mean Bayes risk; optimal quantizers are designed by a novel extension to the Lloyd-Max algorithm. Diversity in the individual agents ’ quantizers leads to optimal performance. 1
KEEP BALLOTS SECRET: ON THE FUTILITY OF SOCIAL LEARNING IN DECISION MAKING BY VOTING
"... We show that social learning is not useful in a model of team binary decision making by voting, where each vote carries equal weight. Specifically, we consider Bayesian binary hypothesis testing where agents have any conditionally-independent observation distribution and their local decisions are fu ..."
Abstract
-
Cited by 2 (2 self)
- Add to MetaCart
(Show Context)
We show that social learning is not useful in a model of team binary decision making by voting, where each vote carries equal weight. Specifically, we consider Bayesian binary hypothesis testing where agents have any conditionally-independent observation distribution and their local decisions are fused by any L-out-of-N fusion rule. The agents make local decisions sequentially, with each allowed to use its own private signal and all precedent local decisions. Though social learning generally occurs in that precedent local decisions af-fect an agent’s belief, optimal team performance is obtained when all precedent local decisions are ignored. Thus, social learning is futile, and secret ballots are optimal. This conclusion contrasts with typical studies of social learning because we include a fusion center rather than concentrating on the performance of the latest-acting agents. Index Terms—Bayesian hypothesis testing, distributed detec-tion and fusion, sequential decision making, social learning, social networks 1.
Distributed Detection with Vector Quantizer
"... Abstract—Motivated by distributed inference over big datasets problems, we study multi-terminal distributed inference problems when each terminal employs vector quantizer. The use of vector quantizer enables us to relax the conditional independence assumption normally used in the distributed detecti ..."
Abstract
- Add to MetaCart
Abstract—Motivated by distributed inference over big datasets problems, we study multi-terminal distributed inference problems when each terminal employs vector quantizer. The use of vector quantizer enables us to relax the conditional independence assumption normally used in the distributed detection with scalar quantizer scenarios. We first consider a case of practical interest in which each terminal is allowed to send zero-rate messages to a decision maker. Subject to a constraint that the error exponent of the type 1 error probability is larger than a certain level, we characterize the best error exponent of the type 2 error probability using basic properties of the r-divergent sequences. We then consider the scenario with positive rate constraints, for which we design schemes to benefit from the less strict rate constraints. Index Terms—Distributed learning, exponential-type con-straints, error exponent, hypothesis testing. I.
DISTRIBUTED HYPOTHESIS TESTING WITH SOCIAL LEARNING AND SYMMETRIC FUSION 1 Distributed Hypothesis Testing with Social Learning and Symmetric Fusion
"... Abstract—We study the utility of social learning in a dis-tributed detection model with agents sharing the same goal: a collective decision that optimizes an agreed upon criterion. We show that social learning is helpful in some cases but is provably futile (and thus essentially a distraction) in ot ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract—We study the utility of social learning in a dis-tributed detection model with agents sharing the same goal: a collective decision that optimizes an agreed upon criterion. We show that social learning is helpful in some cases but is provably futile (and thus essentially a distraction) in other cases. Specifically, we consider Bayesian binary hypothesis testing performed by a distributed detection and fusion system, where all decision-making agents have binary votes that carry equal weight. Decision-making agents in the team sequentially make local de-cisions based on their own private signals and all precedent local decisions. It is shown that the optimal decision rule is not affected by precedent local decisions when all agents observe conditionally independent and identically distributed private signals. Perfect Bayesian reasoning will cancel out all effects of social learning. When the agents observe private signals with different signal-to-noise ratios, social learning is again futile if the team decision is only approved by unanimity. Otherwise, social learning can strictly improve the team performance. Furthermore, the order in which agents make their decisions affects the team decision. Index Terms—Bayesian hypothesis testing, decision fusion, distributed detection, sequential decision making, social learning I.
1Keep Ballots Secret: On the Futility of Social Learning in Decision Making by Voting
"... Abstract—We show that social learning is not useful in a model of team binary decision making by voting, where each vote carries equal weight. Specifically, we consider Bayesian binary hypothesis testing where agents have any conditionally-independent observation distribution and their local decisio ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract—We show that social learning is not useful in a model of team binary decision making by voting, where each vote carries equal weight. Specifically, we consider Bayesian binary hypothesis testing where agents have any conditionally-independent observation distribution and their local decisions are fused by any L-out-of-N fusion rule. The agents make local decisions sequentially, with each allowed to use its own private signal and all precedent local decisions. Though social learning generally occurs in that precedent local decisions affect an agent’s belief, optimal team performance is obtained when all precedent local decisions are ignored. Thus, social learning is futile, and secret ballots are optimal. This contrasts with typical studies of social learning because we include a fusion center rather than concentrating on the performance of the latest-acting agents. Index Terms—Bayesian hypothesis testing, distributed detec-tion and fusion, sequential decision making, social learning, social networks I.
Recommended Citation
, 2014
"... This dissertation is an in-depth case study of NATO advisors and their perceived influence in Afghanistan (2009-2012). It explores the two-part question, how do foreign security actors (ministerial advisors and security force trainers, advisors, and commanders) attempt to influence their host-nation ..."
Abstract
- Add to MetaCart
(Show Context)
This dissertation is an in-depth case study of NATO advisors and their perceived influence in Afghanistan (2009-2012). It explores the two-part question, how do foreign security actors (ministerial advisors and security force trainers, advisors, and commanders) attempt to influence their host-nation partners and what are their perceptions of these approaches on changes in local capacity, values, and security governance norms? I argue that security sector reform (SSR) programs in fragile states lack an explicit theory of change that specifies how reform occurs. From this view, I theorize internationally led SSR as “guided institutional transfer, ” grounded in rationalist and social constructivist explanations of convergence, diffusion, and socialization processes. Responding to calls for greater depth and emphasis on interactions and institutional change in SSR research, I examine NATO’s efforts in Afghanistan as an extreme case of SSR in which external-internal interactions were the highest. A stratified, purposive sample of 68 military and civilian elites (24 ministerial advisors, 27 embedded field advisors and commanders, and 17 experts and external observers) participated in a confidential, semi-structured interview.
