By Michel Regenwetter, Bernard Grofman, A. A. J. Marley, Ilia Tsetlin
During this quantity, whereas taking a look at the probabilistic foundations of collective decision-making ideas, the authors problem a lot of the prevailing theoretical knowledge approximately social selection techniques, and search to revive religion within the threat of democratic selection making. specifically, they argue that concerns in regards to the meant incidence of majority rule cycles that may avoid teams from achieving a last selection approximately what replacement they like were enormously overstated. In perform, majority rule could be anticipated to paintings good in such a lot real-world settings. they supply new insights into how substitute version requirements can switch our estimates of social orderings.
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Extra info for Behavioral social choice : probabilistic models, statistical inference, and applications
We assign to the preference re ano o occur m the profil s of 'necessary conditi , . e. en acknowledges that other natural d eIiIll·tions the term 'necessary on d~re p~~sible \P· 183). We believe that many people in the field use con Itlon m a d1ff . · here, Sen's notion of . erent way, namely m the wav that we use it necessity 1s too str ·f · . · . · ment f o frequencies (prob b"J· . ong I it IS to be checked on a smgle assign a I Itles) . f r near orders, it is easy to sh0 h hto the preference relations in the profile.
Value restriction implies transitivity. We now move from domain restrictions to restrictions on the distribution of preferences, or, more specifically, on the "net preference probabilities" over the full domain. The following definitions are critical throughout the rest of this section. 10 Given a probability lP' on TI, and denoting by n- the reverse order of n, the net ranking probability (net preference probability) N' (induced by JP') is defined as 1 N'(n) = lP'(n)-IP(rr- ). 1: Lack of ~11pport for Ma1ortty 41.
Moreover, the few cycles that have been observe tend to be among alternatives that are low-ranked and among which voters are not readily able to discriminate. " As we mentioned above, Jones et al. (1995) ran Monte Carlo sim~lations in which they drew random samples from a uniform distribu0 ~lOn . ~e~ weak orders. They concluded that making preferences mor: realistic by allowing for weak order preferences (instead of linear orde prefere_nces) reduces the probability of cycles significantly. They argued th~t.
Behavioral social choice : probabilistic models, statistical inference, and applications by Michel Regenwetter, Bernard Grofman, A. A. J. Marley, Ilia Tsetlin