Structured observation is often used to evaluate handwashing behavior. We assessed reactivity to structured observation in rural Bangladesh by distributing soap containing acceleration sensors and performing structured observation 4 days later. Sensors recorded the number of times soap was moved. In 45 participating households, the median number of sensor soap movements during the 5-hour time block on pre-observation days was 3.7 (range 0.3-10.6). During the structured observation, the median number of sensor soap movements was 5.0 (range 0-18.0), a 35% increase, P = 0.0004. Compared with the same 5-hour time block on pre-observation days, the number of sensor soap movements increased during structured observation by ≥ 20% in 62% of households, and by ≥ 100% in 22% of households. The increase in sensor soap movements during structured observation, compared with pre-observation days, indicates substantial reactivity to the presence of the observer. These findings call into question the validity of structured observation for measurement of handwashing behavior.

Deliberation dialogues occur when two or more participants seek to jointly agree on an action or a course of action in some situation. We present the first formal framework for such dialogues, grounding it in a theory of deliberative reasoning from the philosophy of argumentation. We further fully articulate the locutions and rules of a formal dialogue game for this model, so as to specify a protocol for deliberation dialogues. The resulting protocol is suitable for dialogues between computational entities, such as autonomous software agents. To assess our protocol, we consider it against various records of human deliberations, against normative principles for the conduct of human dialogues, and with respect to the outcomes produced by dialogues undertaken according to the protocol.

If you check the weather prediction on your phone, you might read that there is a 30 percent chance of rain at 4:00 in the afternoon. What does this mean? More precisely, what is the fraction 30/100 a measure of? Probability is a concept that is widespread both in everyday life and in science. Ordinary speakers of English utter and have some understanding of sentences such as “I will probably be late for the meeting,” or “it’s unlikely that Luxembourg will win the next World Cup.” Various sciences make explicit probabilistic claims: “the probability that a radium atom will decay in 1620 years is 0.5”; “the probability that a house mouse whose father is heterozygous for the t haplotype will inherit that trait is 0.9.” Other claims implicitly invoke probability: “the life expectancy of a child born in Japan today is 85.3 years.” Probability theory is also a major branch of mathematics, and it was given its modern formulation by Kolmogorov in 1933. Kolmogorov’s formalism presents a function P that satisfies a set of axioms: it is non-negative, normalized, and additive. These axioms entail a rich set of theorems concerning the behavior of P; together they make up the probability calculus. While the resulting theory is a formal theory in its own right, it is also natural to interpret P—to attach meanings, or truth conditions to claims involving it. ‘What is P?’, one may ask. This may be understood as a metaphysical question about what kinds of things are probabilities, or more generally as a question about what makes probability statements true or false. The various interpretations of probability attempt to answer this question, one way or another. This article surveys the literature on the interpretations of probability, due to mathematicians and especially philosophers. It divides the interpretations into two broad categories. Epistemological interpretations understand probability in terms of an agent’s beliefs, the strength of evidence in support of a statement, or other epistemological categories. Physical interpretations view probability as a feature of the world that would exist regardless of what evidence exists or what agents believe. This is a natural taxonomy, but others could be adopted, and its sub-categories are also somewhat pliable. The authors would like to thank Kim Border, Chris Bottomley, Kenny Easwaran, Hanti Lin, Charles Sebens, Glenn Shafer, Julia Staffel, Jeremy Strasser, and an anonymous referee for many helpful suggestions.

No abstract

278 non-freshman university students taking a l2-week critical thinking course in a large single-section class, with computer-assisted guided practice as a replacement for small-group discussion, and all testing in machine-scored multiple-choice format, improved their critical thinking skills, as measured by the California Critical Thinking Skills Test (Forms A and B), by half a standard deviation, a moderate improvement. The improvement was more than that reported with a traditional format without computer-assisted instruction, but less than that reported with a format using both computer-assisted instruction and essay-type assignments. Further studies are needed to test hypotheses suggested by these results.