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 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

Distributed Hypothesis Testing with Social Learning and Symmetric Fusion

We study the utility of social learning in a distributed 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 decisions 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.Comment: 10 pages, 7 figure

Team decision making with social learning: human subject experiments

We demonstrate that human decision-making agents do social learning whether it is beneficial or not. Specifically, we consider binary Bayesian hypothesis testing with multiple agents voting sequentially for a team decision, where each one observes earlier-acting agents' votes as well as a conditionally independent and identically distributed private signal. While the best strategy (for the team objective) is to ignore the votes of earlier-acting agents, human agents instead tend to be affected by others' decisions. Furthermore, they are almost equally affected in the team setting as when they are incentivized only for individual correctness. These results suggest that votes of earlier-acting agents should be withheld (not shared as public signals) to improve team decision-making performance; humans are insufficiently rational to innately apply the optimal decision rules that would ignore the public signals.Accepted manuscrip

Beliefs in Decision-Making Cascades

This work explores a social learning problem with agents having nonidentical noise variances and mismatched beliefs. We consider an $N$-agent binary hypothesis test in which each agent sequentially makes a decision based not only on a private observation, but also on preceding agents' decisions. In addition, the agents have their own beliefs instead of the true prior, and have nonidentical noise variances in the private signal. We focus on the Bayes risk of the last agent, where preceding agents are selfish. We first derive the optimal decision rule by recursive belief update and conclude, counterintuitively, that beliefs deviating from the true prior could be optimal in this setting. The effect of nonidentical noise levels in the two-agent case is also considered and analytical properties of the optimal belief curves are given. Next, we consider a predecessor selection problem wherein the subsequent agent of a certain belief chooses a predecessor from a set of candidates with varying beliefs. We characterize the decision region for choosing such a predecessor and argue that a subsequent agent with beliefs varying from the true prior often ends up selecting a suboptimal predecessor, indicating the need for a social planner. Lastly, we discuss an augmented intelligence design problem that uses a model of human behavior from cumulative prospect theory and investigate its near-optimality and suboptimality.Comment: final version, to appear in IEEE Transactions on Signal Processin

Incorporating Road User Costs into Integrated Life-Cycle Cost Analyses for Infrastructure Sustainability: A Case Study on Sr-91 Corridor Improvement Project (Ca)

Life-cycle cost analysis (LCCA) is a decision-making tool that allows governing agencies the ability to assess several long-term alternative investment options. This paper presents a LCCA analysis process which integrates the Federal Highway Administration (FHWA) program, RealCost (a road user cost calculation program), the FHWA-endorsed Construction Analysis for Pavement Rehabilitation Strategies (CA4PRS) and Caltrans specific design tools (CalFP and CalAC), into the existing Caltrans LCCA process (a modified version of the FHWA LCCA process). In using tools backed by the FHWA and validated through previous agency use, the presented process has a potential to be replicated on urban corridor improvement projects across the US while aiding agencies in achieving economical sustainability throughout the infrastructure maintenance phases. This paper also fills the gap identified by Ozbay et al. in 2004, incorporating road user cost calculations into the LCCA process. Validation was achieved through the execution of the recently completed $1.4 B US California SR-91 Corridor Improvement Project. The SR-91 team used the presented tool to choose one of the two alternatives (maintain HOV SR-91 lane and add I-15 HOV lane using long-life Portland Cement Concrete Pavement or add Express Lane to SR-91 and I-15 using long-life Continuously Reinforced Concrete Pavement and Asphalt Concrete Pavement), equating to an estimated life-cost savings of$32 M.112Nsciessciscopu

Quantization of Prior Probabilities for Collaborative Distributed Hypothesis Testing

This paper studies the quantization of prior probabilities, drawn from an ensemble, for distributed detection and data fusion. Design and performance equivalences between a team of N agents tied by a fixed fusion rule and a more powerful single agent are obtained. Effects of identical quantization and diverse quantization are compared. Consideration of perceived common risk enables agents using diverse quantizers to collaborate in hypothesis testing, and it is proven that the minimum mean Bayes risk error is achieved by diverse quantization. The comparison shows that optimal diverse quantization with K cells per quantizer performs as well as optimal identical quantization with N(K-1)+1 cells per quantizer. Similar results are obtained for maximum Bayes risk error as the distortion criterion.Comment: 11 page

HI Fluctuations at Large Redshifts: II - the Signal Expected for GMRT

For the GMRT, we calculate the expected signal from redshifted HI emission at two frequency bands centered at 610 and 325 MHz. The study focuses on the visibility-visibility cross-correlations, proposed earlier as the optimal statistical estimator for detecting and analyzing this signal. These correlations directly probe the power spectrum of density fluctuations at the redshift where the radiation originated, and thereby provide a method for studying the large scale structures at large redshifts. We present detailed estimates of the correlations expected between the visibilities measured at different baselines and frequencies. Analytic fitting formulas representing the salient features of the expected signal are also provided. These will be useful in planning observations and deciding an optimal strategy for detecting this signal.Comment: 16 pages including 7 figures, published in JAp

Near-saturated and complete genetic linkage map of black spruce (Picea mariana)

<p>Abstract</p> <p>Background</p> <p>Genetic maps provide an important genomic resource for understanding genome organization and evolution, comparative genomics, mapping genes and quantitative trait loci, and associating genomic segments with phenotypic traits. Spruce (<it>Picea</it>) genomics work is quite challenging, mainly because of extremely large size and highly repetitive nature of its genome, unsequenced and poorly understood genome, and the general lack of advanced-generation pedigrees. Our goal was to construct a high-density genetic linkage map of black spruce (<it>Picea mariana</it>, 2n = 24), which is a predominant, transcontinental species of the North American boreal and temperate forests, with high ecological and economic importance.</p> <p>Results</p> <p>We have developed a near-saturated and complete genetic linkage map of black spruce using a three-generation outbred pedigree and amplified fragment length polymorphism (AFLP), selectively amplified microsatellite polymorphic loci (SAMPL), expressed sequence tag polymorphism (ESTP), and microsatellite (mostly cDNA based) markers. Maternal, paternal, and consensus genetic linkage maps were constructed. The maternal, paternal, and consensus maps in our study consistently coalesced into 12 linkage groups, corresponding to the haploid chromosome number (1n = 1x = 12) of 12 in the genus <it>Picea</it>. The maternal map had 816 and the paternal map 743 markers distributed over 12 linkage groups each. The consensus map consisted of 1,111 markers distributed over 12 linkage groups, and covered almost the entire (> 97%) black spruce genome. The mapped markers included 809 AFLPs, 255 SAMPL, 42 microsatellites, and 5 ESTPs. Total estimated length of the genetic map was 1,770 cM, with an average of one marker every 1.6 cM. The maternal, paternal and consensus genetic maps aligned almost perfectly.</p> <p>Conclusion</p> <p>We have constructed the first high density to near-saturated genetic linkage map of black spruce, with greater than 97% genome coverage. Also, this is the first genetic map based on a three-generation outbred pedigree in the genus <it>Picea</it>. The genome length in <it>P. mariana </it>is likely to be about 1,800 cM. The genetic maps developed in our study can serve as a reference map for various genomics studies and applications in <it>Picea a</it>nd Pinaceae.</p

Self-assembled RNA interference microsponges for efficient siRNA delivery

The encapsulation and delivery of short interfering RNA (siRNA) has been realized using lipid nanoparticles1, 2, cationic complexes3, 4, inorganic nanoparticles5, 6, 7, 8, RNA nanoparticles9, 10 and dendrimers11. Still, the instability of RNA and the relatively ineffectual encapsulation process of siRNA remain critical issues towards the clinical translation of RNA as a therapeutic1, 12, 13. Here we report the synthesis of a delivery vehicle that combines carrier and cargo: RNA interference (RNAi) polymers that self-assemble into nanoscale pleated sheets of hairpin RNA, which in turn form sponge-like microspheres. The RNAi-microsponges consist entirely of cleavable RNA strands, and are processed by the cell’s RNA machinery to convert the stable hairpin RNA to siRNA only after cellular uptake, thus inherently providing protection for siRNA during delivery and transport to the cytoplasm. More than half a million copies of siRNA can be delivered to a cell with the uptake of a single RNAi-microsponge. The approach could lead to novel therapeutic routes for siRNA delivery.National Institutes of Health (U.S.) (NIH) NIBIB Grant R01-EB008082)United States. American Recovery and Reinvestment Act of 2009 ((ARRA) grant)National Science Foundation (U.S.) (Division of Materials Research Polymers Program grant #0705234)David H. Koch Institute for Integrative Cancer Research at MIT (Nanotechnology grant