Prediction and Topological Models in Neuroscience

In the last two decades, philosophy of neuroscience has predominantly focused on explanation. Indeed, it has been argued that mechanistic models are the standards of explanatory success in neuroscience over, among other things, topological models. However, explanatory power is only one virtue of a scientific model. Another is its predictive power. Unfortunately, the notion of prediction has received comparatively little attention in the philosophy of neuroscience, in part because predictions seem disconnected from interventions. In contrast, we argue that topological predictions can and do guide interventions in science, both inside and outside of neuroscience. Topological models allow researchers to predict many phenomena, including diseases, treatment outcomes, aging, and cognition, among others. Moreover, we argue that these predictions also offer strategies for useful interventions. Topology-based predictions play this role regardless of whether they do or can receive a mechanistic interpretation. We conclude by making a case for philosophers to focus on prediction in neuroscience in addition to explanation alone

Imaging of a Transitional Disk Gap in Reflected Light: Indications of Planet Formation Around the Young Solar Analog LkCa 15

We present H- and Ks-band imaging data resolving the gap in the transitional disk around LkCa 15, revealing the surrounding nebulosity. We detect sharp elliptical contours delimiting the nebulosity on the inside as well as the outside, consistent with the shape, size, ellipticity, and orientation of starlight reflected from the far-side disk wall, whereas the near-side wall is shielded from view by the disk's optically thick bulk. We note that forward-scattering of starlight on the near-side disk surface could provide an alternate interpretation of the nebulosity. In either case, this discovery provides confirmation of the disk geometry that has been proposed to explain the spectral energy distributions (SED) of such systems, comprising an optically thick outer disk with an inner truncation radius of ~46 AU enclosing a largely evacuated gap. Our data show an offset of the nebulosity contours along the major axis, likely corresponding to a physical pericenter offset of the disk gap. This reinforces the leading theory that dynamical clearing by at least one orbiting body is the cause of the gap. Based on evolutionary models, our high-contrast imagery imposes an upper limit of 21 Jupiter masses on companions at separations outside of 0.1" and of 13 Jupiter masses outside of 0.2". Thus, we find that a planetary system around LkCa 15 is the most likely explanation for the disk architecture.Comment: 5 pages, 4 figures, accepted for publication in ApJ Letters. Minor change to Figure

Towards understanding of value co-creation on web 2.0 platforms: an assessment methodology

Web 2.0 technologies and social media can be used as a platform for value creation. While some firms are successful in engaging with external audiences on social media, others are less so. Many of these firms have equal access to web 2.0 platforms and operate in similar or same conditions, the resource based view (RBV) offers an explanation for disparities in performance of these organisations: the differences are due to firm internal, valuable, immutable and rare resources. Comparing high- and low-performers in a comparative case study helps to identify and highlight firm internal idiosyncratic resources that result in better sustained performances. In-depth studies inside the organisations answer the questions how and why some firms on the same platform can attract higher engagement levels than others. The problem is how to recognise best and worst performance to conduct in-depth case studies? This paper introduces ALIAS – a methodology for identification of the relative firm performance within a population, and selection of theoretically relevant cases to conduct comparative case studies through the lens of RBV. The proposed methodology is a five step process and utilises the DART framework of value co-creation for identification and assessment of performance criteria

Essays on Executive Compensation

I study executive compensation in various situations, including the cases where (i) CEOs have relative wealth concerns (RWCs); (ii)inside debt can be a part of an optimal contract; (iii) there are ambiguous information about firm value. The first chapter, Relative Wealth Concerns and Executive compensation , studies the implications of RWCs on executive compensation. I first study the case in which a CEO\u27s effort increases firm value without changing firm risk. In this case, RWCs will result in an increase in CEO incentives. This effect is larger if aggregate risk is higher, so RWCs can lead to a positive relation between CEO incentives and aggregate risk. CEOs with RWCs willingly risk exposure to aggregate shock to keep up with their peers. This help to reduce risk premium paid to the CEOs. As a result, RWCs can be beneficial to shareholders\u27 payoffs. I also provide a simple explanation for the pay-for-luck puzzle. I next examine the case in which the CEO\u27s effort affects both the mean and variance of firm value. I show that RWCs can lead to a negative relation between CEOs\u27 risk-taking behavior and their incentives, which is consistent with some empirical evidence. Lastly, I show that RWCs render options preferable to stock where aggregate risk is much larger than idiosyncratic risk. The second chapter, Inside Debt , is a coauthored paper with my advisor Alex Edmans. We justify the use of debt as efficient compensation. We show that inside debt is a superior solution to the agency costs of debt than the solvency-contingent bonuses proposed by prior literature, since its payoff depends not only on the incidence of bankruptcy but also firm value in bankruptcy. Contrary to intuition, granting the manager equal proportions of debt and equity is typically inefficient. The optimal ratio depends on the trade off of the importance between project selection and effort. The model generates a number of empirical predictions consistent with recent evidence. In the last chapter, Incentive Contracting under Ambiguity-Aversion , I study the effect of ambiguity on the pay structure of executive compensation. I show that when there is ambiguity in firm risk and if a manager is risk-averse and ambiguity-averse, stock-based contracts always impose a high risk premium. But option-based contracts can induce the manager to perceive a low risk and thus pay a low risk premium, which makes options less costly than stock. I also show that a manager tends to perceive a higher risk when she is granted higher incentives, which makes shareholders reluctant to grant managers high incentives and take advantage of some small improvements. As a result, compensation contracts exhibit an inertia property. Lastly, if ambiguity comes from the expected market returns, tying CEO pay to the market is optimal, which provides an explanation for the pay-for-luck puzzle

Storing and Indexing Plan Derivations through Explanation-based Analysis of Retrieval Failures

Case-Based Planning (CBP) provides a way of scaling up domain-independent planning to solve large problems in complex domains. It replaces the detailed and lengthy search for a solution with the retrieval and adaptation of previous planning experiences. In general, CBP has been demonstrated to improve performance over generative (from-scratch) planning. However, the performance improvements it provides are dependent on adequate judgements as to problem similarity. In particular, although CBP may substantially reduce planning effort overall, it is subject to a mis-retrieval problem. The success of CBP depends on these retrieval errors being relatively rare. This paper describes the design and implementation of a replay framework for the case-based planner DERSNLP+EBL. DERSNLP+EBL extends current CBP methodology by incorporating explanation-based learning techniques that allow it to explain and learn from the retrieval failures it encounters. These techniques are used to refine judgements about case similarity in response to feedback when a wrong decision has been made. The same failure analysis is used in building the case library, through the addition of repairing cases. Large problems are split and stored as single goal subproblems. Multi-goal problems are stored only when these smaller cases fail to be merged into a full solution. An empirical evaluation of this approach demonstrates the advantage of learning from experienced retrieval failure.Comment: See http://www.jair.org/ for any accompanying file

When do children learn from unreliable speakers?

Children do not necessarily disbelieve a speaker with a history of inaccuracy; they take into account reasons for errors. Three- to 5 year-olds (N = 97) aimed to identify a hidden target in collaboration with a puppet. The puppet’s history of inaccuracy arose either from false beliefs, or occurred despite his being fully informed. On a subsequent test trial, children’s realistic expectation about the target was contradicted by the puppet who was fully informed. Children were more likely to revise their belief in line with the puppet’s assertion when his previous errors were due to false beliefs. Children who explained this puppet’s prior inaccuracy in terms of false belief were more likely to believe the puppet than those who did not. As children’s understanding of the mind advances, they increasingly balance the risk of learning falsehoods from unreliable speakers against that of rejecting truths from speakers who made excusable errors