How moral illusions make us less effective Stijn Bruers Stijnbruers.wordpress.com Stijn.bruers@gmail.com

Discrimination (speciesism)

Empathy

Unwanted arbitrariness

Arbitrary categorization and nationalism Whole world Land mass (Eurasia) Continent (Europe) Country (Belgium) Region (Flanders) Municipality (Ghent) ???

Arbitrary categorization and religious conflicts all beliefs religions Abrahamists Christians Catholics Roman-Catholics ???

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

Your grand- mother Your mother You

Irrational fear

Irrational fear Smallpox vaccine No interpersonal violence (world peace) 10% of deaths 1% of deaths Eradicating smallpox = 10 times world peace!

Irrational fear Violence free world? Ebola free world? 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 8% of DALYs 9% of DALYs

Non vegan world Vegan world

Compassion fade and psychic numbing

Compassion fade and psychic numbing

Compassion fade and psychic numbing Letter A: save Rokia Letter B: save Rokia and Moussa 100€ 80€ (40€ for Rokia) 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.

Compassion fade and psychic numbing

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). Measuring non-use damages using contingent valuation: experimental evaluation accuracy. Research Triangle Institute Monograph 92-1.

Identifiable victim effect Kogut T. & Ritov I (2005). The “identified victim” effect: an identified group, or just a single individual? Journal of Behavioral Decision Making 18 (3): 157–167.

Zero risk bias Disease A: affects 1% of people Vaccine A: reduces disease 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: reduces disease 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.

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

Zero risk bias

Arbitrary categorization all suffering type (diseases) class (infectious diseases) transmission (viral diseases) species (disease A) subspecies (disease A1) ???

Cause neutrality

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

Asian disease problem Intervention A 200 of 600 lives saved Expectation value: 1/3 of people saved Intervention B 1/3 probability of saving 600 lives Expectation value: 1/3 of people saved

Asian disease problem Intervention C 400 of 600 people die Expectation value: 2/3 of people die Intervention D 2/3 probability 600 people die Expectation value: 2/3 of people die

Futility thinking Intervention A: helps 1000 of 3000 people 33% of people saved 1000 people saved Intervention B: helps 2000 of 100.000 people 2% of people saved 2000 people saved 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.

Certainty effect (Allais paradox) Policy A: everyone receives 1000€ Policy B: 50% receive 3000€, 50% receive nothing

Certainty effect (Allais paradox) Policy A: 10% of people receive 1000€ Policy B: 5% receive 3000€, 95% receive nothing

Existential risk Probability: 0,000000001 (P1) Number of future lives at stake: 1000000000000000000000000 (N) Expected number of lives lost (P1xN): 1000000000000 (E1) 1% reduction of risk; new probability (P2): 0,00000000099 New expectated number of lives lost (P2xN): 990000000000 (E2) Expected number of lives ‘saved’ (E1-E2): 10000000000

Population ethics Variable populations Maximize total well-being?

Population ethics The repugnant conclusion (Derek Parfit) 10 10 8 9

Population ethics The repugnant conclusion 9 7 8 1

Intransitivity

Status quo bias (reversal test) Value ??? Parameter Bostrom N. & Ord T. (2006). The reversal test: eliminating status quo bias in applied ethics. Ethics 116 (4): 656–679.

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