Based on a survey of 237 managers in Singapore, three measures of organizational ethics (namely, top management support for ethical behavior, the organization''s ethical climate, and the association between ethical behavior and career success) are found to be associated with job satisfaction. The link between organizational ethics and job satisfaction is argued from Viswesvaran et al.''s (1998) organizational justice and cognitive dissonance theories. The findings imply that organizational leaders can favorably influence organizational outcomes by engaging in, supporting and rewarding ethical (...) behavior. (shrink)

How should a machine intelligence perform unsupervised structure discovery over streams of sensory input? One approach to this problem is to cast it as an apperception task [1]. Here, the task is to construct an explicit interpretable theory that both explains the sensory sequence and also satisfies a set of unity conditions, designed to ensure that the constituents of the theory are connected in a relational structure. However, the original formulation of the apperception task had one fundamental limitation: it assumed (...) the raw sensory input had already been parsed using a set of discrete categories, so that all the system had to do was receive this already-digested symbolic input, and make sense of it. But what if we don't have access to pre-parsed input? What if our sensory sequence is raw unprocessed information? The central contribution of this paper is a neuro-symbolic framework for distilling interpretable theories out of streams of raw, unprocessed sensory experience. First, we extend the definition of the apperception task to include ambiguous (but still symbolic) input: sequences of sets of disjunctions. Next, we use a neural network to map raw sensory input to disjunctive input. Our binary neural network is encoded as a logic program, so the weights of the network and the rules of the theory can be solved jointly as a single SAT problem. This way, we are able to jointly learn how to perceive (mapping raw sensory information to concepts) and apperceive (combining concepts into declarative rules). (shrink)

This paper attempts to answer a central question in unsupervised learning: what does it mean to “make sense” of a sensory sequence? In our formalization, making sense involves constructing a symbolic causal theory that both explains the sensory sequence and also satisfies a set of unity conditions. The unity conditions insist that the constituents of the causal theory – objects, properties, and laws – must be integrated into a coherent whole. On our account, making sense of sensory input is a (...) type of program synthesis, but it is unsupervised program synthesis. Our second contribution is a computer implementation, the Apperception Engine, that was designed to satisfy the above requirements. Our system is able to produce interpretable human-readable causal theories from very small amounts of data, because of the strong inductive bias provided by the unity conditions. A causal theory produced by our system is able to predict future sensor readings, as well as retrodict earlier readings, and impute (fill in the blanks of) missing sensory readings, in any combination. In fact, it is able to do all three tasks simultaneously. We tested the engine in a diverse variety of domains, including cellular automata, rhythms and simple nursery tunes, multi-modal binding problems, occlusion tasks, and sequence induction intelligence tests. In each domain, we test our engine's ability to predict future sensor values, retrodict earlier sensor values, and impute missing sensory data. The Apperception Engine performs well in all these domains, significantly out-performing neural net baselines. We note in particular that in the sequence induction intelligence tests, our system achieved human-level performance. This is notable because our system is not a bespoke system designed specifically to solve intelligence tests, but a general-purpose system that was designed to make sense of any sensory sequence. (shrink)

This thesis focuses on the question of whether moral feelings are necessary to the making of moral judgments. This is an important question and the answer one gives has more interesting implications than one might initially expect. I will argue that an experientialist account of moral concepts, on which moral judgments are beliefs about objective facts represented by moral feelings, provides the best naturalistic answer to the question. To make my point, I anchor my arguments in a series of comparisons (...) between the experientialist account and its rivals, and how they handle metaethical puzzles to do with the moral status of psychopaths, moral twin earth, the nature of moral motivation, and issues of integrity and moral improvement. (shrink)

This paper attempts to decipher what we really mean when we use the word "Science" by briefly exploring the criterion of "predictive power" with respect to the demarcation problem. It is essentially an articulation of Lakatos' view of Science and attempts to show that predictive power is quintessential to science.

Ecology has become a popular conceptual model in numerous fields of inquiry and it seems especially appropriate for environmental philosophy. Apart from its literal employment in biology, ecology has served as a useful metaphor that captures the interdependence of factors in a field of research. At the same time as ecology is suggestive, it cannot be followed literally or blindly. This paper considers the appropriateness of the uses to which ecology has been put in some recent discussions of architectural and (...) environmental aesthetics, and develops a critique of the differing ecological aesthetics of Jusuck Koh and Xiangzhan Cheng. (shrink)