The goal of this paper is to provide a general discussion about the legitimacy of corporate social responsibility. Given that social responsibility projects entail costs, it is not always obvious under what precise conditions managers will have a responsibility to engage in activities primarily designed to promote societal goals.In this paper we discuss four distinct criteria for evaluating the legitimacy of corporate projects for institutionalizing social responsibility.

An empirical study of whether the ordinary conception of race in the United States is biological or social, and how different conceptions connect to racism.

Mr. C is a twenty‐two‐year‐old who was flown to a level‐1 trauma center after diving headfirst into shallow water. Prior to this accident, he was in excellent health. At the scene, he had been conscious but was paralyzed and had no sensation below his neck. The emergency medical services team immobilized Mr. C's neck with a cervical collar and intubated him for airway protection before transport. As Mr. C's medical care proceeds, he expresses a desire for extubation, although it was (...) not clear that he had the capacity to make this (or any other) decision. It also was unclear whether this desire reflected his authentic wishes to be allowed to die or stemmed from feelings of discomfort and agitation. Over a period of several days, Mr. C's sedation was lightened, and the psychiatry service conducted a formal decision‐making capacity assessment. Mr. C continues to express a desire to withdraw life‐sustaining medical treatment. However, the psychiatry service is concerned that Mr. C seems to lack adequate insight into what it would mean to die.What does it mean to respect Mr. C's autonomy? (shrink)

We formulate a new modal ontological argument; specifically, we show that there is a possible world in which an entity that has at least the property of omnipotence exists. Then we argue that if such an entity is possible, it is necessary as well.

It is generally assumed that common stock investors are exclusively interested in earning the highest level of future cash-flow for a given amount of risk. This view suggests that investors select a well-diversified portfolio of securities to achieve this goal. Accordingly, it is often assumed that investors are unwilling to pay a premium for corporate behavior which can be described as socially-responsible.Recently, this view has been under increasing attack. According to the Social Investment Forum, at least 538 institutional investors now (...) allocate funds using social screens or criteria. In addition, Alice Tepper Marlin, president of the New York-based Council on Economic Priorities has recently estimated that about $600 billion of invested funds are socially-screened (1992). (shrink)

Most cognitive psychology experiments evaluate models of human cognition using a relatively small, well-controlled set of stimuli. This approach stands in contrast to current work in neuroscience, perception, and computer vision, which have begun to focus on using large databases of natural images. We argue that natural images provide a powerful tool for characterizing the statistical environment in which people operate, for better evaluating psychological theories, and for bringing the insights of cognitive science closer to real applications. We discuss how (...) some of the challenges of using natural images as stimuli in experiments can be addressed through increased sample sizes, using representations from computer vision, and developing new experimental methods. Finally, we illustrate these points by summarizing recent work using large image databases to explore questions about human cognition in four different domains: modeling subjective randomness, defining a quantitative measure of representativeness, identifying prior knowledge used in word learning, and determining the structure of natural categories. (shrink)

In many learning or inference tasks human behavior approximates that of a Bayesian ideal observer, suggesting that, at some level, cognition can be described as Bayesian inference. However, a number of findings have highlighted an intriguing mismatch between human behavior and standard assumptions about optimality: People often appear to make decisions based on just one or a few samples from the appropriate posterior probability distribution, rather than using the full distribution. Although sampling-based approximations are a common way to implement Bayesian (...) inference, the very limited numbers of samples often used by humans seem insufficient to approximate the required probability distributions very accurately. Here, we consider this discrepancy in the broader framework of statistical decision theory, and ask: If people are making decisions based on samples—but as samples are costly—how many samples should people use to optimize their total expected or worst-case reward over a large number of decisions? We find that under reasonable assumptions about the time costs of sampling, making many quick but locally suboptimal decisions based on very few samples may be the globally optimal strategy over long periods. These results help to reconcile a large body of work showing sampling-based or probability matching behavior with the hypothesis that human cognition can be understood in Bayesian terms, and they suggest promising future directions for studies of resource-constrained cognition. (shrink)

Current psychological theories of human causal learning and judgment focus primarily on long-run predictions: two by estimating parameters of a causal Bayes nets, and a third through structural learning. This paper focuses on people’s short-run behavior by examining dynamical versions of these three theories, and comparing their predictions to a real-world dataset.

Universities are expected to be places where knowledge is shared freely among academicians. However, the reality shows that knowledge sharing is barely present within universities these days. As Malaysia shifts towards building a knowledge-based society, academic institutions, particularly the public universities, now face ever-growing faculty demands for sharing quality resources and expertise. As a result, knowledge sharing in academia has become a rising concern. The purpose of this study, then, is to uncover the factors that propel knowledge sharing among academicians (...) in higher learning institutions of Malaysia. Using the Theory of Reasoned Action as the basis for this study’s research framework, data was gathered from 447 academicians in 10 public universities scattered throughout the country. Findings from the Partial Least Squares analysis revealed that extrinsic motivation, reciprocal relationships, sense of self-worth and subjective norm are vital determinants of an academician’s attitude towards knowledge sharing. In turn, this attitude that is formed will largely determine whether the academician engages in knowledge sharing behaviors or not. Besides having a positive effect on attitude, sense of self-worth also exhibited a positive impact on the subjective norm to share knowledge. Consequently, this subjective norm will have an impact upon knowledge sharing behavior. In addition, perceived behavioral control and organizational climate were discovered to have a direct influence on knowledge sharing behaviors. Implications, limitations as well as suggestions for future research are accordingly discussed in this paper. (shrink)

We investigated people's ability to infer others’ mental states from their emotional reactions, manipulating whether agents wanted, expected, and caused an outcome. Participants recovered agents’ desires throughout. When the agent observed, but did not cause the outcome, participants’ ability to recover the agent's beliefs depended on the evidence they got. When the agent caused the event, participants’ judgments also depended on the probability of the action ; when actions were improbable given the mental states, people failed to recover the agent's (...) beliefs even when they saw her react to both the anticipated and actual outcomes. A Bayesian model captured human performance throughout, consistent with the proposal that people rationally integrate information about others’ actions and emotional reactions to infer their unobservable mental states. (shrink)

This participatory study, conducted in an introductory psychology class, recounts self-reflections of 22 undergraduate students and their instructor engaging in an GenAI-mediated storyboard generation process. It relies on Gordon Pask’s conversation theory, structuring out the nature of interactions between students, instructor, and GenAI, and then uses a qualitative narrative to describe these conversational feedback loops constituting the creation of draft and final storyboards. Results suggest students engaged in cyclical feedback driven processes to master their creations, used elements of photography related (...) to the relationships between objects in frame, image processing, and cultural objects/themes to generate narratives. Issues were faced with generating text on images, and the consistency and style of generated frames as they interacted with GenAI tools. This study provides guidelines to university teaching professionals to design effective activities mediated by text-to-image GenAI. (shrink)

For more than sixty years, surgeons have used bioethical strategies to promote patient self‐determination, many of these now collectively described as “informed consent.” Yet the core framework—understanding, risks, benefits, and alternatives—fails to support patients in deliberation about treatment. We find that surgeons translate this framework into an overly complicated technical explanation of disease and treatment and an overly simplified narrative that surgery will “fix” the problem. They omit critical information about the goals and downsides of surgery and present untenable options (...) as a matter of patient choice. We propose a novel framework called “better conversations.” Herein, surgeons provide context about clinical norms, establish the goals of surgery, and comprehensively delineate the downsides of surgery to generate a deliberative space for patients to consider whether surgery is right for them. This paradigm shift meets the standards for informed consent, supports deliberation, and allows patients to anticipate and prepare for the experience of treatment. (shrink)

A Psychologico-Ethical Study Of Human Parasitism, Its Effects, Causes And Cure; And A Philosophy Of Human Life And Aspirations Based Thereupon.

The correspondence theory of truth is a precise and innovative account of how the truth of a proposition depends upon that proposition's connection to a piece of reality. Joshua Rasmussen refines and defends the correspondence theory of truth, proposing new accounts of facts, propositions, and the correspondence between them. With these theories in hand, he then offers original solutions to the toughest objections facing correspondence theorists. Addressing the Problem of Funny Facts, Liar Paradoxes, and traditional epistemological questions concerning how (...) our minds can access reality, he challenges recent objections, and defends what has traditionally been the most popular theory of truth. Written with clarity, precision, and sensitivity to a range of philosophical backgrounds, his book will appeal to advanced students and scholars seeking a deeper understanding of the relationship between truth and reality. (shrink)

Shepard has argued that a universal law should govern generalization across different domains of perception and cognition, as well as across organisms from different species or even different planets. Starting with some basic assumptions about natural kinds, he derived an exponential decay function as the form of the universal generalization gradient, which accords strikingly well with a wide range of empirical data. However, his original formulation applied only to the ideal case of generalization from a single encountered stimulus to a (...) single novel stimulus, and for stimuli that can be represented as points in a continuous metric psychological space. Here we recast Shepard's theory in a more general Bayesian framework and show how this naturally extends his approach to the more realistic situation of generalizing from multiple consequential stimuli with arbitrary representational structure. Our framework also subsumes a version of Tversky's set-theoretic model of similarity, which is conventionally thought of as the primary alternative to Shepard's continuous metric space model of similarity and generalization. This unification allows us not only to draw deep parallels between the set-theoretic and spatial approaches, but also to significantly advance the explanatory power of set-theoretic models. Key Words: additive clustering; Bayesian inference; categorization; concept learning; contrast model; features; generalization; psychological space; similarity. (shrink)

Today, liberals face a predicament: how to defend liberal principles, when adherence to them seems to constitute a fatal disadvantage against unprincipled opponents. The challenge is not new. In the early years of the twentieth century, liberalism was attacked, by critics on both the right and, especially, the left for being hypocritical, naïve, irresponsible, and impotent. It couldn't, for example (anti-liberalists thought), address the acute inequality of imperial rule, racial segregation, and socio-economic poverty. These issues of social justice it was (...) claimed by critics required a politics marked by an uncompromising commitment to ultimate ends, and an unrelenting use of power. Faced with such sentiments and the practical successes of anti-liberal ideologies (i.e. Fascism, Nazism, and Communism) liberals felt pressure to silence their scruples and doubts, and embrace the confidence, ruthlessness, and intransigence exhibited by their opponents. But doing so seemed tantamount to abandoning liberal hopes for, and commitments to, human freedom and all they valued in the first place. In Liberalism for Dark Times, Cherniss tells the story of the liberal response to this challenge in the twentieth century. Through a close study of five leading intellectuals engaged in these debates-Max Weber, Raymond Aron, Albert Camus, Reinhold Niebuhr, and Isaiah Berlin-Cherniss reconstructs a distinctive, neglected strand of liberal thought. This strand defines and defends liberalism as a political ethos: a complex of dispositions, temperament, and sensibility and style-which include skepticism; openness to experience; and careful, discriminating judgment-that shape how individuals make choices, meet challenges, understand and pursue possibilities, and conduct themselves toward others in the course of political struggle. In reconstructing the history of, what he calls, a tempered liberalism, and formulating it as a distinctive political perspective, Cherniss offers an alternative to the prevalent ways of thinking about both, liberalism's history and the intellectual resources available to it today. (shrink)

Decades of psychological research have been aimed at modeling how people learn features and categories. The empirical validation of these theories is often based on artificial stimuli with simple representations. Recently, deep neural networks have reached or surpassed human accuracy on tasks such as identifying objects in natural images. These networks learn representations of real‐world stimuli that can potentially be leveraged to capture psychological representations. We find that state‐of‐the‐art object classification networks provide surprisingly accurate predictions of human similarity judgments for (...) natural images, but they fail to capture some of the structure represented by people. We show that a simple transformation that corrects these discrepancies can be obtained through convex optimization. We use the resulting representations to predict the difficulty of learning novel categories of natural images. Our results extend the scope of psychological experiments and computational modeling by enabling tractable use of large natural stimulus sets. (shrink)