Decision Making in Star Networks with Incorrect Beliefs
نویسندگان
چکیده
Consider a Bayesian binary decision-making problem in star networks, where local agents make selfish decisions independently, and fusion agent makes final decision based on aggregated its own private signal. In particular, we assume all have beliefs for the true prior probability, which they perform making. We focus Bayes risk of counterintuitively find that incorrect could achieve smaller than when know prior. It is independent interest sociotechnical system design optimal resemble human probability reweighting models from cumulative prospect theory. also consider asymptotic characterization agent's number agents. converges to zero exponentially fast as grows. Furthermore, having an identical constant belief asymptotically sense exponent. For additive Gaussian noise, turns out be simple function only error costs exponent can explicitly characterized.
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ژورنال
عنوان ژورنال: IEEE Transactions on Signal Processing
سال: 2021
ISSN: ['1053-587X', '1941-0476']
DOI: https://doi.org/10.1109/tsp.2021.3123891