The text of A. Blok's inaugural lecture (Wittgenstein en Elias: een methodische richtlijn voor de anthropologie [Wittgenstein and Elias: A Methodological Direction for Anthropology], Assen: Van Gorcum, 1975) is reviewed. Blok contends that both nominalism & essentialism lead one astray & he attempts to guide one along a new track that avoids the pitfalls of both. However, the notion of 'family resemblance' (Wittgenstein's notion) that he uses, does not succeed in accounting for general terms since the boundaries of a concept so defined are inherently vague. Blok is criticized for overemphasizing the descriptive part of sociology at the expense of theory. Blok remains an old-style empiricist & fails to perceive that empirical data may be the result of the interaction of many factors that are not directly experienceable. In Van karikatuur tot kompas: een kritiek op Bloks keenistheoretische en methodische koers (From Caricature to Compass: Critique of Blok's Methodological and Epistemological Course), H. Tromp notes several misconceptions regarding nominalism. He also points out that the notion of family resemblance presupposes the presence of a theory, ie, it takes a certain perspective to say that A resembles B (otherwise everything resembles everything else). In Een nieuwe weg of nieuwe woorden? Kanttekeningen bij een oratie (A New Way, or New Words? Marginal Notes to a Sermon), A. J. F. Kobben argues that Blok's attacks are directed at strawmen & that the notion of family resemblance itself is not new in anthropology. In Repliek (Reply) A. Blok answers that it is possible to give a coherent picture of a society without appealing to laws & generalizations (as is done in some novels, eg, Dickens's work). The use of definitions is accepted as long as they can be justified by their authors. Cross-cultural studies benefit from the family resemblance approach by making it easier for the knowledge of one phenomenon to help understand another phenomenon that only 'resembles' it, yet is subsumed under the same concept. A. O.


Sorcery and warfare are closely interrelated in the Eastern Highlands of Papua New Guinea. In contrast to other areas of the Highlands, sorcery in large parts of the Eastern Highlands is held to be an exclusively male domain, and violent retribution for deaths attributed to sorcery is primarily directed against other communities. Sorcery accusations thus have the tendency to escalate to large-scale inter-group warfare often causing additional casualties. Sorcery beliefs have undergone changes during the colonial and postcolonial era, with new forms of sorcery proliferating, and the zones of safety from sorcery shrinking due to demographic and economic changes. Sorcery accusations were triggers for the resumption of warfare during the late 1970s and 1980s, and they remain pertinent to outbreaks of hostilities today. In fact, the majority of armed conflicts between 1975 and 2006 among a sample of Fore, Auyana and Tairora communities in the Okapa and Obura-Wonenara districts of the Eastern Highlands Province are connected to sorcery beliefs and sorcery accusations. These sorcery accusations are the result of uneven economic development and failure to deliver basic social services. When violence is threatened, local leaders try to mediate the hostility, but the state seems unable to offer alternatives for the peaceful settlement of conflicts.


State-action value functions (i.e., Q-values) are ubiquitous in reinforcement learning (RL), giving rise to popular algorithms such as SARSA and Q-learning. We propose a new notion of action value defined by a Gaussian smoothed version of the expected Q-value. We show that such smoothed Q-values still satisfy a Bellman equation, making them learnable from experience sampled from an environment. Moreover, the gradients of expected reward with respect to the mean and covariance of a parameterized Gaussian policy can be recovered from the gradient and Hessian of the smoothed Q-value function. Based on these relationships we develop new algorithms for training a Gaussian policy directly from a learned smoothed Q-value approximator. Our approach is amenable to proximal optimization techniques by augmenting the objective with a penalty on KLdivergence from a previous policy. We find that the ability to learn both a mean and covariance during training allows this approach to achieve much better results on standard continuous control benchmarks.