A Map of Update Constraints in Inductive Inference

We investigate how different learning restrictions reduce learning power and how the different restrictions relate to one another. We give a complete map for nine different restrictions both for the cases of complete information learning and set-driven learning. This completes the picture for these well-studied \emph{delayable} learning restrictions. A further insight is gained by different characterizations of \emph{conservative} learning in terms of variants of \emph{cautious} learning. Our analyses greatly benefit from general theorems we give, for example showing that learners with exclusively delayable restrictions can always be assumed total.Comment: fixed a mistake in Theorem 21, result is the sam

Learn Without Counterfactuals

In this paper we study learning procedures when counterfactuals (payo s of not-chosen actions) are not observed. The decision maker reasons in two steps: First, she updates her propensities for each action after every payo experience, where propensity is de ned as how much she prefers each action. Then, she transforms these propensities into choice probabilities. We introduce natural axioms in the way propensities are updated and the way propensities are translated into choice, and study the decision marker's behavior when such axioms are in place.Adaptive Learning; Counterfactuals; Partial Information; Reinforcement Learning

Sequential games under positional uncertainty

This dissertation focuses on sequential games of imperfect information. I study settings in which not only do agents face imperfect information in the traditional sense of not possessing all payoff-relevant information, but they also face uncertainty about their position of movement in the sequence. I have utilized this framework to study financial investment decisions by individuals, production decisions by firms, and implications on information aggregation in observational learning. In order to study production decisions by firms I utilize a Stackelberg oligopoly model with a stochastic consumer demand. In this setting firms do not know their position of movement, and as a result of the stochastic demand they cannot infer from the prevailing price if another firm has yet entered the market. I find that as a result of uncertainty firms produce a higher quantity than they otherwise would have, resulting in a more competitive outcome. In fact, as the number of firms in the market increases, with positional uncertainty the equilibrium quantity actually exceeds the perfectly competitive quantity. I then investigate the impact of positional uncertainty when agents must choose levels of investment in a financial asset. Investors receive a signal about the value of the asset but are not necessarily aware of their position in the sequence of investors. As a result, they are unsure to what extent the signal they receive represents profit-relevant information, or if the signal is “stale” in the sense that the information has been incorporated into the price by other investors. This results in more cautious levels of investment, and an asset price that does not represent the true underlying value. To study the behavioral aspects of financial investment, I introduce in this model a notion of confidence. While much work in the area of behavioral finance has studied the role of confidence over the accuracy of information, my interest is in confidence over the timing of information. I define an agent as overconfident if they believe they are more likely to have received the signal earlier than other agents, and are thus more likely to be early investors. The effect of overconfidence can overwhelm the cautious nature of positionally uncertain investors, even potentially leading to an overreaction to information. This effect can explain overvaluation of assets and volatility of prices in response to information. In a model of observational learning, limited information about the history of actions slows the integration of information. However, I show that in the limit, even in the presence of limited histories complete learning occurs. In the environment of limited access to historical information I introduce uncertainty over position of action. This uncertainty even further dampens the process of learning from a welfare standpoint, but as the number of agents grows large complete learning still obtains in the limit for all levels of uncertainty. The common finding in all these settings is that uncertainty about the order of action causes agents to be cautious about exploiting profitable opportunities. In the case of oligopoly this leads to more competitive outcomes, whereas in the cases of investment and social learning uncertainty leads to less effective information aggregation

Energetics of the brain and AI

Does the energy requirements for the human brain give energy constraints that give reason to doubt the feasibility of artificial intelligence? This report will review some relevant estimates of brain bioenergetics and analyze some of the methods of estimating brain emulation energy requirements. Turning to AI, there are reasons to believe the energy requirements for de novo AI to have little correlation with brain (emulation) energy requirements since cost could depend merely of the cost of processing higher-level representations rather than billions of neural firings. Unless one thinks the human way of thinking is the most optimal or most easily implementable way of achieving software intelligence, we should expect de novo AI to make use of different, potentially very compressed and fast, processes

The free recall search process introduces errors in short term memory but apparently not in long term memory

Here it is reported that the free recall search process increases the error rate for short term memory (about 1% per second in data from Murdock & Okada (1970)) but not for long term memory (in data from McDermott (1996)). If the short term memory search process introduces random excitations, which would account for the search errors, the subjects should be unaware of making such errors. This is in agreement with DRM findings (Gallo, 2010) and the new finding that the error terminated distributions in Murdock (1962) are the same as those terminated by studied items

PENGARUH MENSTRUASI TERHADAP MINAT SISWA PUTRI DALAM PEMBELAJARAN PENDIDIKAN JASMANI, OLAHRAGA DAN KESEHATAN (STUDI PADA SISWA PUTRI KELAS X SMA NEGERI 1 BANGIL )

Abstract Interest factor will give big effect to student learning process. Interest will encourage student learning better than without interest. During following physical education, movement limitation indirectly can affect student interest. Just like female student who think that menstruation as something that can limit their activity, especially during following physical education learning. During this time at school often seen female student that not following physical education with various reasons like stomach ache, worried, and uncomfort so that they reluctant to performed physical education.  This research aim to find out the effect of menstruation to female student interest on physical education. This is a qualitative research with descriptive qualitative method. Subject in this research is female student of SMA Negeri I Bangil that amounted of 10 students. While the process data collecting conducted by guided free interview on each subject.   Research results that obtained : from 10 respondents, each experienced emotional disorder during menstruation, both pain that caused by menstruation or cautious and anxious due affraid to soaked that cause student to lazy to move and tend to rest. Thus, menstruation can alter female student interest in following physical education, that at first have high interest in following the lesson become decrease. So, there is an effect of menstruation to female student interest in following physical, sports and health education learning. Keywords:menstruation, interest, physical sport, and health education