论文标题
在有偏见的信息提供商存在下，理性代理的最佳决策
Optimal Decisions of a Rational Agent in the Presence of Biased Information Providers
论文作者
论文摘要
我们考虑了信息网络，其中多个有偏见的信息提供者（BIPS），例如媒体/社交网络用户/传感器，与理性信息消费者（RICS）共享事件的报告。使事件可以作为逻辑语句的答案报告合理的抽象，我们将每个BIP的投入输出行为建模为二进制通道。由于各种原因，有些BIP可能会分享事件的不正确报告。此外，每个BIP在报告时有利于两个结果之一，或者“无偏见”，如果它不偏爱两种结果，则“有偏见”。由于用户特征/世界观的差异，此类偏见发生在信息/社交网络中。 我们研究BIPS偏见对RIC选择的影响，同时推论真实信息。我们的工作表明，“图形” RIC在其邻居中寻找$ n $ bips，以特殊的方式行事，以最大程度地减少其在推论真实信息时犯错误的可能性。首先，我们确定了违反直觉的事实，即通过选择完全偏向于A-Priori可能事件的BIP来最大程度地减少RIC的预期错误。然后，我们研究了完全偏见的BIP在公正的BIP上提供的收益，当它们的二进制频道的错误率均等，以公平比较，以$ r> 0 $。具体而言，RIC预期错误概率的无偏见比率随指数$ \ frac {n} {n} {2} {2} \ ln \ left（4ρ_0^2 \ left（\ frac {1} {1} {1} {r} {r} {r} {r} -1 \ right）$ 0的$ peation $ pres nes $ p e Event p e Eventepection（4ρ_0^2} \ ln \ left（\ frac {1} {1} {1} {1} {1} {1} {1} {1} {1}这不仅表明完全偏见的BIP比无偏见或异质偏见的BIP更可取，而且对于小$ r $而言，收益可能是可观的。
We consider information networks whereby multiple biased-information-providers (BIPs), e.g., media outlets/social network users/sensors, share reports of events with rational-information-consumers (RICs). Making the reasonable abstraction that an event can be reported as an answer to a logical statement, we model the input-output behavior of each BIP as a binary channel. For various reasons, some BIPs might share incorrect reports of the event. Moreover, each BIP is: 'biased' if it favors one of the two outcomes while reporting, or 'unbiased' if it favors neither outcome. Such biases occur in information/social networks due to differences in users' characteristics/worldviews. We study the impact of the BIPs' biases on an RIC's choices while deducing the true information. Our work reveals that a "graph-blind" RIC looking for $n$ BIPs among its neighbors, acts peculiarly in order to minimize its probability of making an error while deducing the true information. First, we establish the counter-intuitive fact that the RIC's expected error is minimized by choosing BIPs that are fully-biased against the a-priori likely event. Then, we study the gains that fully-biased BIPs provide over unbiased BIPs when the error rates of their binary channels are equalized, for fair comparison, at some $r>0$. Specifically, the unbiased-to-fully-biased ratio of the RIC's expected error probabilities grows exponentially with the exponent $\frac{n}{2}\ln\left(4ρ_0^2\left(\frac{1}{r}-1\right)\right)$, where $ρ_0$ is the event's prior probability of being $0$. This shows not only that fully-biased BIPs are preferable to unbiased or heterogeneously-biased BIPs, but also that the gains can be substantial for small $r$.
