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1. Information avoidance and overvaluation under epistemic constraints: Principles and implications for regulatory policies

   https://doi.org/10.1016/j.ress.2020.106814  

   Pozzi, Matteo ; Malings, Carl ; Minca, Andreea ( January 2020 , Reliability engineering systems safety)

   The Value of Information (VoI) assesses the impact of data in a decision process. A risk-neutral agent, quantifying the VoI in monetary terms, prefers to collect data only if their VoI surpasses the cost to collect them. For an agent acting without external constraints, data have non-negative VoI (as free “information cannot hurt”) and those with an almost-negligible potential effect on the agent's belief have an almost-negligible VoI. However, these intuitive properties do not hold true for an agent acting under external constraints related to epistemic quantities, such as those posed by some regulations. For example, a manager forced to repair an asset when its probability of failure is too high can prefer to avoid collecting free information about the actual condition of the asset, and even to pay in order to avoid this, or she can assign a high VoI to almost-irrelevant data. Hence, by enforcing epistemic constraints in the regulations, the policy-maker can induce a range of counter-intuitive, but rational, behaviors, from information avoidance to over-evaluation of barely relevant information, in the agents obeying the regulations. This paper illustrates how the structural properties of VoI change depending on such external epistemic constraints, and discusses how incentives and penalties can alleviate these induced attitudes toward information. 

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2. Neuronal Firing Rate as Code Length: A Hypothesis

   Qian, Ning ; Zhang, Jun ( January 2019 , Computational brain & behavior)

   Many theories assume that a sensory neuron’s higher firing rate indicates a greater probability of its preferred stimulus. However, this contradicts 1) the adaptation phenomena where prolonged exposure to, and thus increased probability of, a stimulus reduces the firing rates of cells tuned to the stimulus; and 2) the observation that unexpected (low probability) stimuli capture attention and increase neuronal firing. Other theories posit that the brain builds predictive/efficient codes for reconstructing sensory inputs. However, they cannot explain that the brain preserves some information while discarding other. We propose that in sensory areas, projection neurons’ firing rates are proportional to optimal code length (i.e., negative log estimated probability), and their spike patterns are the code, for useful features in inputs. This hypothesis explains adaptation-induced changes of V1 orientation tuning curves, and bottom-up attention. We discuss how the modern minimum-description-length (MDL) principle may help understand neural codes. Because regularity extraction is relative to a model class (defined by cells) via its optimal universal code (OUC), MDL matches the brain’s purposeful, hierarchical processing without input reconstruction. Such processing enables input compression/understanding even when model classes do not contain true models. Top-down attention modifies lower-level OUCs via feedback connections to enhance transmission of behaviorally relevant information. Although OUCs concern lossless data compression, we suggest possible extensions to lossy, prefix-free neural codes for prompt, online processing of most important aspects of stimuli while minimizing behaviorally relevant distortion. Finally, we discuss how neural networks might learn MDL’s normalized maximum likelihood (NML) distributions from input data. 

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3. Misinterpreting Others and the Fragility of Social Learning

   https://doi.org/10.3982/ECTA16981  

   Frick, Mira ; Iijima, Ryota ; Ishii, Yuhta ( January 2020 , Econometrica)

   null (Ed.)

   We exhibit a natural environment, social learning among heterogeneous agents, where even slight misperceptions can have a large negative impact on long‐run learning outcomes. We consider a population of agents who obtain information about the state of the world both from initial private signals and by observing a random sample of other agents' actions over time, where agents' actions depend not only on their beliefs about the state but also on their idiosyncratic types (e.g., tastes or risk attitudes). When agents are correct about the type distribution in the population, they learn the true state in the long run. By contrast, we show, first, that even arbitrarily small amounts of misperception about the type distribution can generate extreme breakdowns of information aggregation, where in the long run all agents incorrectly assign probability 1 to some fixed state of the world, regardless of the true underlying state. Second, any misperception of the type distribution leads long‐run beliefs and behavior to vary only coarsely with the state, and we provide systematic predictions for how the nature of misperception shapes these coarse long‐run outcomes. Third, we show that how fragile information aggregation is against misperception depends on the richness of agents' payoff‐relevant uncertainty; a design implication is that information aggregation can be improved by simplifying agents' learning environment. The key feature behind our findings is that agents' belief‐updating becomes “decoupled” from the true state over time. We point to other environments where this feature is present and leads to similar fragility results. 

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4. Multi-agent Value of Information for components’ inspections

   Lin, C ; Balcan, M-F ; Blum, A. ; Pozzi, M. ( August 2023 , ICASP14 - 14th International Conference on Applications of Statistics and Probability in Civil Engineering)

   We assess the Value of Information (VoI) for inspecting components in systems managed by multiple agents, using game theory and Nash equilibrium analysis. We focus on binary systems made up by binary components which can be either intact or damaged. Agents taking maintenance actions are responsible for the repair costs of their own components, and the penalty for system failure is shared among all agents. The precision of inspection is also considered, and we identify the prior and posterior Nash equilibrium with perfect or imperfect inspections. The VoI is assessed for the individual agents as well as for the whole set of agents, and the analysis consider series, parallel and general systems. A negative VoI can trigger the phenomenon of Information Avoidance (IA), where rational agents prefer not to collect free information. We discuss whether it is possible that the VoI is negative for one or for all agents, for the agents with inspected or uninspected components, and for the total sum of VoIs. 

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5. Supporting Student Internships with the Nsf Hsi Program at a Medium-Sized Hispanic-Serving Institution

   Cruz, Alberto C. ; Derickson, Dennis ; Malek, Amin ( June 2023 , 2023 ASEE Annual Conference & Exposition)

   Our work describes the best-practices and findings for a recent NSF IUSE HSI HRD grant. Its overarching goal is to drive an institutional change where the University proactively places students in internships with local industry partners. Students at the University are non-traditional, minority and low-income. They often working twenty to forty hours a week in non-curricular jobs. The Grant Program fully or partially subsidizes compensation for interns through financial aid scholarships. It aims to replace non-curricular work with relevant, real-world engineering experiences. This in turn improves their prospects to find jobs post-graduation. Modern students work while going to school. A small amount of work—less than fifteen hours a week—is beneficial. However, beyond twenty hours a week has a negative impact. Hispanic/Latino(a) students work twenty to forty hours a week, more than any other demographic. This workload affects attendance, GPA, and utility, resulting in poor workforce placement. Academia must concede that work comes first for under-represented students. Universities must take steps to supplant irrelevant work experience with industry internships. Participants of this program received relevant internship/work experience, had better retention rates due to perceived utility of their degree. In the long term we expect timely to graduation due to participants taking internship units as credit toward their degree. Students learned of the internships from faculty members soliciting applications to the program, supported by the grant. Executing the MOU between the University and industry partners took considerable effort and is a major barrier to executing formal partnerships between internship hosts. One MOU is still in negotiation since the start of the program. Despite some student participants reporting prior internship experiences, no one involved in the program would have found an internship this academic year without help from the Grant Program. Some students claimed to have submitted from twenty to fifty applications and the Grant Program was the only internship that called for an interview. Quality of internship varied from corporation to corporation. Universities must carefully monitor the feedback of participants to ensure that the individual goals of the participants are being met. Finding corporations that are willing to invest time in mentorship of students is a critical component to ensure student satisfaction. Even so, regardless of internship quality, participants would not have found internships if not for the Grant Program. According to the participants, internships are an opportunity to network and build lasting professional connections. While students may be unable to turn every internship into a full-time position, each experience will give them something much more valuable and long-lasting: relationships with professionals and co-workers. The connections they make during their time at an organization can be stepping stones to their next opportunity. 

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