000			nam a22 4500
999			_c114227 _d114227
003			OSt
005			20190213151912.0
008			190122b ||||| |||| 00| 0 eng d
020			_a9781107495906
040			_c
082			_a368 _bTHO
100			_aThomas, Guy _934610
245			_aLoss Coverage: Why Insurance Works Better With Some Adverse Selection
260			_bCambridge University Press _c2017 _aCambridge
300			_a274p
500			_aPart I. Introduction: 1. The central ideas of this book 2. Adverse selection: a history of exaggeration Part II. Loss Coverage: 3. Introduction to loss coverage; 4. Basic mathematics of loss coverage 5. Further mathematics of loss coverage 6. Partial risk classification, separation and inclusivity Part III. Further Aspects of Risk Classification: 7. A taxonomy of objections to risk classification 8. Empirical evidence on adverse selection 9. Myths of insurance rhetoric 10. Myths of insurance economics 11. Contexts where adverse selection may be stronger 12. Risk classification and moral hazard 13. Risk classification and big data Part IV. Conclusion: 14. Summary and suggestions
600			_aInsurance - Social aspects _935242
942			_2ddc _cLB _k368 _mTHO