Learning to deal with risk: what does reinforcement learning tell us about risk atittudes?.

People are generally reluctant to accept risk. In particular, people overvaluate outcomes that are considered certain, relative to outcomes which are merely probable. At the same time, people is also more willing to accept bets when payoffs involve losses rather than gains. I consider how far adaptive learning can go on in explaining these phenomena. I report simulations in which adaptive learners of the kind studied in Roth & Erev (1995, 1998) and B6rgers & Sarin (1996, 1997) deal with a problem of iterated choice under risk where alternatives differ by a mean preserving spread. The simulations show that adaptive learning induce (on average) risk averse choices. This learning bias is stronger for gains than for losses. Also, risk averse choices are much more likely when one of the alternatives is a certain prospect. The implications of a learning interpretation of risk taking are examined.Iterated choice; reinforcement learning; risk attitudes;

Peningkatkan Prestasi Belajar Akuntansi Dengan Teknik Bimbingan Self Contracting and Self Reinforcement

The objective of the study was to improve students' learnig achievement to complete the Accounting cycle at service companies through the guidance techniques of self- contracting and self reinforcement on the 1st semester students of XI PE 3class in SMK Negeri 1 Pemalang in the academic year of 2011/2012. The previous assessments of students' learning results in completing the Accounting Cycle for service companies showed low average, it was only 65.40, whereas the minimum score was 75. The low achievement could be initiated by students' low motivation and activities. They were reluctant to study because of the domination of lecturing method. Thus, it was needed a more appropriate method to develop the exemplary, to give motivation, and also to give opportunity for students to be optimal in learning. The learning process through the guidance technique of self- contracting and self reinforcement could increase the students' achievement with the average of daily score was 75.98, it was higher than the KKM (minimum score) and the students' completeness was 77.50% which increased about 32.50

Students' Schemata Activation in Extensive Reading at Stain Ponorogo

This study was aimed at investigating the extent to which the lecturer employed strategy and occupied effective classroom language to assist students' schemata activation on Extensive Reading class at English Department­STAIN Ponorogo. To meet with the objectives, qualita­tive case study formed the methodological basis of this present research involving an extensive reading lecturer as the research subject with one of her respective classes consisted of 32 students of fourth semester. The data were derived from lecturer's utterances (verbal) and body lan­guage (nonverbal). Those data were obtained from the transcripts of the recorded lecturer's utterances during two periods of meeting, and note­taking taken from observations and interviews. The results revealed that the lecturer used to employ questioning technique to activate students' schemata. Various strategies were predominantly occupied in lecturer's questioning behaviors to engage students in question­answer activities, such as probing, redirecting and reinforcement. Generally, those strate­gies were observed not only to provide motivation and focus students' at­tention towards the topic being discussed, but also to give a wide chance of opportunity for them to recall their prior knowledge and ease them to predict the content of reading texts they were going to read. Besides, the language the lecturer used under this investigation was fairly fulfilled the requirements to be communicative as some communicative features of talks were utilized properly, such as referential questions, content feed­back, and speech modification to optimize students' participation and performance in the process of reading

Meta Inverse Reinforcement Learning via Maximum Reward Sharing for Human Motion Analysis

This work handles the inverse reinforcement learning (IRL) problem where only a small number of demonstrations are available from a demonstrator for each high-dimensional task, insufficient to estimate an accurate reward function. Observing that each demonstrator has an inherent reward for each state and the task-specific behaviors mainly depend on a small number of key states, we propose a meta IRL algorithm that first models the reward function for each task as a distribution conditioned on a baseline reward function shared by all tasks and dependent only on the demonstrator, and then finds the most likely reward function in the distribution that explains the task-specific behaviors. We test the method in a simulated environment on path planning tasks with limited demonstrations, and show that the accuracy of the learned reward function is significantly improved. We also apply the method to analyze the motion of a patient under rehabilitation.Comment: arXiv admin note: text overlap with arXiv:1707.0939

Grading Changes after a Writing Faculty Workshop

After a workshop on student outcomes for the first-year writing course, the 28 faculty participants discussed the implications of “Development” for critical thinking. This case study of one college’s participatory exercise in improving writing found that although the RWU faculty lacked consensus on the definition, simply discussing topic of “Development” may have had the unintended effect of fewer A grades in the following semester. Unfortunately, the percentage of A grades ascended in the subsequent semesters to suggest that without reinforcement, faculty returned to grade inflation

Using rewards and penalties to obtain desired subject performance

Operant conditioning procedures, specifically the use of negative reinforcement, in achieving stable learning behavior is described. The critical tracking test (CTT) a method of detecting human operator impairment was tested. A pass level is set for each subject, based on that subject's asymptotic skill level while sober. It is critical that complete training take place before the individualized pass level is set in order that the impairment can be detected. The results provide a more general basis for the application of reward/penalty structures in manual control research

On ‘common-sense ontology’:A comment on the paper by Frank Hindriks and Francesco Guala

This note comments on Hindriks and Guala’s ‘unified theory of institutions’. One of the components that Hindriks and Guala seek to unify, and which they claim is unsatisfactory on its own, is the analysis of conventions that derives from the work of Lewis. I argue that the Lewisian approach provides simple and powerful explanations of many regularities in the social behaviour of humans and other animals. Those explanations can be seen as good social science even if, as Hindriks and Guala argue, they do not fit with common-sense ontology

Improving basic life support training for medical students

Mariam Lami, Pooja Nair, Karishma GadhviFaculty of Medicine, Imperial College, London, London, UKAbstract: Questions have been raised about basic life support (BLS) training in medical education. This article addresses the research evidence behind why BLS training is inadequate and suggests recommendations for improving BLS training for medical students.Keywords: medical education, basic life suppor