1. How moral illusions make us less effective
Stijn Bruers
Stijnbruers.wordpress.com

3. Discrimination (speciesism)

5. Empathy

6. Unwanted arbitrariness

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

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

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

10. Your grand-
mother
Your
mother
You

11. Irrational fear

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

13. 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

14. Non vegan world
Vegan world

15. Compassion fade and psychic numbing

16. Compassion fade and psychic numbing

17. 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): e doi: /journal.pone
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

18. Compassion fade and psychic numbing

20. 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.

21. 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.

22. 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,

23. Zero risk bias Perceived badness of risk Risk Vaccine B Vaccine A0% 1%
20%
22%
Risk
Problem A
Problem B

24. Zero risk bias

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

26. Cause neutrality

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

28. 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

29. 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

30. Futility thinking Intervention A: helps 1000 of 3000 people
33% of people saved
1000 people saved
Intervention B: helps 2000 of 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:
Unger, P. (1996). Living High and Letting Die, Oxford: Oxford University Press.

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

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

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

34. Population ethics
Variable populations Maximize total well-being?

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

36. Population ethics
The repugnant conclusion 9 7 8 1

37. Intransitivity

38. 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.

39. Questions?
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