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How moral illusions make us less effective

How moral illusions make us less effective. Stijn Bruers Stijnbruers.wordpress.com Stijn.bruers@ gmail.com. Discrimination ( speciesism ). Empathy. Unwanted arbitrariness.

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How moral illusions make us less effective

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  1. Howmoralillusionsmakeuslesseffective Stijn Bruers Stijnbruers.wordpress.com Stijn.bruers@gmail.com

  2. Discrimination (speciesism)

  3. Empathy

  4. Unwantedarbitrariness

  5. For everychoiceyou make, you have tobeabletogive a justifyingrule of whichyoucanconsistently want thateveryonefollowsthatrule in allpossiblesituations

  6. Arbitrarycategorization and nationalism Whole world Land mass (Eurasia) Continent (Europe) Country (Belgium) Region (Flanders) Municipality (Ghent) ???

  7. Arbitrarycategorization and religiousconflicts all beliefs religions Abrahamists Christians Catholics Roman-Catholics ???

  8. all life kingdom (animals) phylum (vertebrates) class (mammals) order (primates) family (great apes) genus (Homo) species(Homo sapiens) ethnic group (whites) ???

  9. Your grand- mother Your mother You

  10. Arbitrarycategorizationanddiseases all suffering type (diseases) class (infectious diseases) transmission (viral diseases) species (disease A) subspecies (disease A1) ???

  11. Causeneutrality

  12. Arbitrarinessandanimalsuffering All suffering Animal suffering Anthropogenic animal suffering Animal shelters

  13. Irrationalfear

  14. Irrationalfear • Smallpox vaccine • No interpersonalviolence (worldpeace) 10% of deaths 1% of deaths Eradicatingsmallpox = 10 timesworldpeace!

  15. Irrationalfear Disability Adjusted Life Years • Violence free world? • Ebola free world? • AIDS free world? • Smoke free world? • Hunger free world? • Accident free world? • Vegan world? 1% of DALYs 0% of DALYs 3% of DALYs 5% of DALYs 5% of DALYs 8% of DALYs 9% of DALYs

  16. Non vegan world Vegan world

  17. Overhead aversion

  18. Compassion fade and psychicnumbing

  19. Compassion fade and psychicnumbing

  20. Compassion fade and psychicnumbing • Letter A: save Rokia • Letter B: save Rokia and Moussa • 100€ • 80€ (40€ forRokia) Västfjäll D, Slovic P, Mayorga M, Peters E (2014) Compassion Fade: Affect and Charity Are Greatest for a Single Child in Need. PLoS ONE 9(6): e100115. doi:10.1371/journal.pone.0100115 Slovic, P. (2007), If I Look at Mass I Will Never Act: Psychic Numbing and Genocide. In Judgment and Decision Making, Volume 2, no. 2, pp. 79-95.

  21. Compassion fade and psychicnumbing

  22. Scope neglect • Letter A: save 2000 birds • Letter B: save 20000 birds • $80 • $78 Desvousges, W. Johnson, R. Dunford, R. Boyle, K. J. Hudson, S. and Wilson K. N. (1992). Measuringnon-usedamagesusing contingent valuation: experimentalevaluationaccuracy. Research TriangleInstituteMonograph 92-1.

  23. Identifiablevictim effect 1 child far away Kogut T. & Ritov I (2005). The “identified victim” effect: an identified group, or just a single individual? Journal of BehavioralDecisionMaking 18 (3): 157–167.

  24. Zero risk bias • Disease A: affects 1% of people • Vaccine A: reducesdisease A with 100% (from 1% to 0%) • Total reduction of (risk of) all diseases: 1% (from 23% to 22%) • Disease B: affects 22% of people • Vaccine B: reducesdisease B with 10% (from 22% to 20%) • Total reduction of (risk of) all diseases: 2% (from 23% to 21%) Kahneman, D. &Tversky, A. (1979) Prospect theory: An analysis of decision under risk, Econometrica, 47, 263-291.

  25. Zero risk bias Perceived badness of risk Vaccine B Vaccine A 0% 1% 20% 22% Risk Problem A Problem B

  26. Zero risk bias

  27. Law of diminishingmarginalutility • Bill Gates: 114 dollar per second • Buying house = buyingloaf of bread Happiness Money

  28. Framing effectsand risk aversion Tversky A. & Kahneman D. (1981). The Framing of decisions and the psychology of choice. Science 211 (4481): 453–458.

  29. Asiandiseaseproblem • Intervention A • 200 of 600 lives saved • Expectationvalue: 1/3 of peoplesaved • Intervention B • 1/3 probability of saving 600 lives • Expectationvalue: 1/3 of peoplesaved

  30. Asiandiseaseproblem • Intervention C • 400 of 600 people die • Expectationvalue: 2/3 of people die • Intervention D • 2/3 probability 600 people die • Expectationvalue: 2/3 of people die

  31. Prospect theory • Description of people’schoicesunderuncertaintybased on potentialvalue of relativelossesandgainsusing heuristics • Daniel Kahneman, Amos Tversky

  32. Prospect theory • Dilemma 1: • Choice A: I will save 0 people • Choce B: 50% probability I willkill 10 people, 50% probability I will save 10 people • Dilemma 1’: • Choice A’: I will save 10 people • Choice B’: 50% probability I will save 20 people, 50% probability I will save 0

  33. Narrowbracketing • Evaluatingdecisionsseparately • Mathew Rabin, Georg Weiszäcker • Dilemma 2: • Choice C: I willkill 9 people • Choice D: 50% probability I willkill 20 people, 50% I willkill no-one

  34. Narrowbracketing • Dilemma 1-2 combined: • Choice AC: losing 9 lives • Choice AD: 50% losing 20 lives, 50% losing 0 lives • Choice BC: 50% losing 19 lives, 50% saving 1 live • Choice BD: 25% losing 30 lives, 50% losing 10 lives, 25% saving 10 lives

  35. Futility thinking • Intervention A: helps 1000 of 3000 people • 33% of peoplesaved • 1000 peoplesaved • Intervention B: helps 2000 of 100.000 people • 2% of peoplesaved • 2000 peoplesaved Fetherstonhaugh, D., Slovic, P., Johnson, S. and Friedrich, J. (1997). Insensitivity to the value of human life: A study of psychophysical numbing. Journal of Risk and Uncertainty, 14: 238-300. Unger, P. (1996). Living High and Letting Die, Oxford: Oxford University Press.

  36. Certainty effect (Allais paradox) • Policy A: everyonereceives 1000€ • Policy B: 50% receive 3000€, 50% receivenothing

  37. Certainty effect (Allais paradox) • Policy A: 10% of peoplereceive 1000€ • Policy B: 5% receive 3000€, 95% receivenothing

  38. Existential risk • Probability: 0,000000001 (P1) • Number of future lives at stake: 1000000000000000000000000 (N) • Expectednumber of lives lost (P1xN): 1000000000000 (E1) • 1% reduction of risk; newprobability (P2): 0,00000000099 • New expectatednumber of lives lost (P2xN): 990000000000 (E2) • Expectednumber of lives ‘saved’ (E1-E2): 10000000000

  39. Populationethics Variablepopulations Maximizetotalwell-being?

  40. Populationethics 100% 70% 70% 90% 90% 100% 80% 80% 80% 80% 80% 90% 90% Derek Parfit

  41. Populationethics 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1%

  42. Intransitivity

  43. Populationethics The repugnantconclusion (Derek Parfit) 10 10 8 9

  44. Populationethics The repugnantconclusion 9 7 8 1

  45. Populationethics Total utilitarianismandthesadisticconclusion +10 +1 -10

  46. Populationethics Averageutilitarianismandthesadisticconclusion 10 8 5 10 -10

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