User:Alexander L. Davis
- Alexander L. Davis
- Carnegie Mellon University
- 5000 Forbes Avenue
- Pittsburgh, PA, United States.
- Email me through OpenWetWare
I work in Social and Decision Sciences at Carnegie Mellon University. I learned about OpenWetWare from Wikipedia and John Miller. My main interest is creativity and hypothesis generation with respect to normative, descriptive, and prescriptive decision-making, focusing on scientific decisions.
- 2012, PhD, Carnegie Mellon University, Behavioral Decision Research
- 2009, MS, Carnegie Mellon University, Behavioral Decision Research
- 2007, BS, Northern Arizona University, Psychology
Research Interests and Lab Notebooks
Psychology of Methodology
What are the important psychological aspects of designing, implementing, interpreting, and reporting experimental research?
Surprises, Error, and Data Sharing
To what degree are unexpected data attributed to methodological error in foresight and hindsight, and how does this relate to decisions to share the data?
Incentives, Error, and Data Sharing
How do humans behave when they can prevent harm to others by incurring it on themselves?
Generosity Across Contexts
Are Preferences for Harming Others Rational?
Human Behavior and Electricity Consumption
What are the cognitive and motivational factors involved in understanding one's electricity consumption?
Setting a Standard for Pilot Studies
Experiments on In-Home Display Design
Predicting Volunteering in Energy Efficiency Programs
An Ideal Field Trial
Methodology of Psychology
How can prescriptive approaches to scientific research help our cognitive and social limitations? I'm writing a book. Not sure what to call it yet. I'll make it available, for free, and I'd very much appreciate comments, critiques, suggestions or whatever. Since it is free, if you feel inclined to show your gratitude to me financially, I suggest donating to kiva.org or your favorite charity.
The File-Drawer Problem (Dissertation)
Do we share data when we should?
Courses I want to make
My take on everything you need to know to complete an experimental research project.
Adaptive pretesting: Adaptive design for pretesting: How can we use adaptive design to develop very strong experiments efficiently? We may want to test our auxiliary assumptions 'online' until they converge into a reasonable risk level. Stats for Social Sciences: Rest assured there will be no p-values. Cohen et al, applied regression Advanced Stats: Rest assured there will be no p-values. Cosma's Class; Bayesian Data Analysis; Gelman and Hill; Intro to Cognitive Psychology: Human thinking, reasoning, perception, etc. Intro to Social Psychology: Motivation, social cognition, etc. Intro to Experimental Economics: Real human behavior in microeconomics and game theory. Kagel and Roth: Handbook of Expeirmental Economics; Plott and Smith: Handbook of experiment economics results Computer Science for Social Sciences: Python, Octave, R; Stats; Complexity Theory; Sipser: Introduction to the theory of computation Decision Theory: Raiffa; Levi? Seidenfeld; Savage; Ramsey; Berger: Statistical Decision Theory and Bayesian Analysis; Gilboa: Theory of decision under uncertainty; Pratt and Raiffa: Statistical Decision Theory; Kadane (2011; pg. 1-4) has a nice demonstration of how to be a dutch bookie. Behavioral Decision Research: Normative, Descriptive, Prescriptive; Poulton; K&T; von Winterfeldt and Edwards; Raiffa and Tversky: Decision-Making; Gigerenzer: Adaptive Thinking Game Theory: Von Neumann and Morgenstern; Luce and Raiffa: Games and Decisions Behavioral Game Theory: Camerer: Behavioral Game Theory; Builds on Decision Theory with empirical evidence. Prereqs: Decision theory; stats Computational Cognitive Science: Covers three of the most successful computational models: ACT-R; Church; Connectionist; Anderson: How can the mind exist? Psychology of Science: Herb Simon; Gorman; Klahr; Wason; Klayman and Ha;Carruthers, Stich and Siegal: The cognitive basis of science Philosophy of Science: Peirce; Carnap; Wittgenstein; Quine; Duhem; Lakatos; Kuhn; Popper; Van Bovens and Hartmann; Mayo; Suppes; Machine Learning: MacKay: information theory, inference and learning algorithms; Hastie, Tibshirani and Friedman: The elements of statistical learning; Bishop: Pattern recognition and Machine Learning; Human and Artificial Intelligence; How can human performance be elucidated by comparing it to modern artificial intelligence methods. For examaple, how does human knowledge representation compare to an ontology? Applications include drug discovery.
- Preparing for smart grid technologies: A behavioral decision research approach to understanding consumer expectations about smart meters http://www.sciencedirect.com/science/article/pii/S0301421511009244