Думай «почему?». Причина и следствие как ключ к мышлению

MacKinnon, D. (2008). Introduction to Statistical Mediation Analysis. Lawrence Erlbaum Associates, New York, NY.

McDonald, R. (2001). Structural equations modeling. Journal of Consumer Psychology 10: 92–93.

Muthén, B., and Asparouhov, T. (2015). Causal effects in mediation modeling. Structural Equation Modeling 22: 12–23.

Pearl, J. (1998). Graphs, causality, and structural equation models. Sociological Methods and Research 27: 226–284.

Pearl, J. (2001). Direct and indirect effects. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann, San Francisco, CA, 411–420.

Pearl, J. (2014). Interpretation and identification of causal mediation. Psychological Methods 19: 459–481.

Robins, J., and Greenland, S. (1992). Identifiability and exchangeability for direct and indirect effects. Epidemiology 3: 143–155.

Rubin, D. (2004). Direct and indirect causal effects via potential outcomes. Scandinavian Journal of Statistics 31: 161–170.

Rubin, D. (2005). Causal inference using potential outcomes: Design, modeling, decisions. Journal of the American Statistical Association 100: 322–331.

Shpitser, I. (2013). Counterfactual graphical models for longitudinal mediation analysis with unobserved confounding. Cognitive Science 37: 1011–1035.

Terman, L., to Tolman, R. (August 6, 1943). Correspondence. Lewis M. Terman Archives, Stanford University.

VanderWeele, T. (2014). A unification of mediation and interaction: A four-way decomposition. Epidemiology 25: 749–761.

VanderWeele, T. (2015). Explanation in Causal Inference: Methods for Mediation and Interaction. Oxford University Press, New York, NY.

Welling, D., MacKay, P., Rasmussen, T., and Rich, N. (2012). A brief history of the tourniquet. Journal of Vascular Surgery 55: 286–290.

Глава 10. Большие данные, искусственный интеллект и важные вопросы

Annotated Bibliography

An accessible source for the perpetual free will debate is Harris (2012). The compatibilist school of philosophers is represented in the writings of Mumford and Anjum (2014) and Dennett (2003).

Artificial intelligence conceptualizations of agency can be found in Russell and Norvig (2003) and Wooldridge (2009). Philosophical views on agency are compiled in Bratman (2007). An intent-based learning system is described in Forney et al. (2017).

The twenty-three principles for “beneficial AI” agreed to at the 2017 Asilomar meeting can be found at Future of Life Institute (2017).

References

Bratman, M. E. (2007). Structures of Agency: Essays. Oxford University Press, New York, NY.

Brockman, J. (2015). What to Think About Machines That Think. HarperCollins, New York, NY.

Dennett, D. C. (2003). Freedom Evolves. Viking Books, New York, NY.

Forney, A., Pearl, J., and Bareinboim, E. (2017). Counterfactual datafusion for online reinforcement learners. Proceedings of the 34th International Conference on Machine Learning. Proceedings of Machine Learning Research 70: 1156–1164.

Future of Life Institute. (2017). Asilomar AI principles. Available at: https://futureoflife.org/ai-principles (accessed December 2, 2017). Harris, S. (2012). Free Will. Free Press, New York, NY.

Mumford, S., and Anjum, R. L. (2014). Causation: A Very Short Introduction (Very Short Introductions). Oxford University Press, New York, NY.

Russell, S. J., and Norvig, P. (2003). Artificial Intelligence: A Modern Approach. 2nd ed. Prentice Hall, Upper Saddle River, NJ. Wooldridge, J. (2009). Introduction to Multi-agent Systems. 2nd ed. John Wiley and Sons, New York, NY.