Online matching markets

Transcription

1 Online matching markets Christian Helmers 19 May / 18

2 Online matching markets 2 / 18

3 Online matching markets 3 / 18

4 Introduction Digitization of matching markets Labor markets (odesk, mechanical turk, Elance, GetACode, etc.) Dating markets (Tinder, OKcupid, Grindr, Whim, Hinge, MeetUp, Match, etc.) Lots of other types of matching markets... Impact of digitization Thicker markets Less frictions Do digital matching markets create efficient allocation? Can allocation be improved? 4 / 18

5 Matching markets Two sides of a market need to be matched Each side cares about to whom it is matched Allocate indivisible goods without explicit price Challenge is in determining an allocation mechanism Can alternative mechanisms improve allocation? Types of two-sided matching: One-to-one matching One-to-many matching Many-to-many matching 5 / 18

6 One-to-one matching (marriage model) 2 players: men M = {m 1,..., m n }, women W = {w 1,..., w p } Two-sided market, men have only preferences over set W {m i } (same for women) Preferences P(m) = {w 1, w 2, m, w 3,..., w m } Strict preferences (complete and transitive) Woman w acceptable to man m if he likes her at least as well as remaining single (and vice versa) Outcome is matching: set of matched pairs (m, w) such that each individual has one partner (if man m is matched to woman w, woman w is matched to man m) Matching stable if individual prefers match over being single and if pair prefers match over alternative match Theorem (Gale and Shapley, 1962): a stable matching exists for every marriage market Stable match is efficient 6 / 18

7 One-to-one matching (marriage model) Stable matching (Gale and Shapley, 1962): deferred acceptance algorithm Each man proposes to the most preferred woman Woman accepts most highly ranked offer and rejects rest or all offers Each rejected man removes woman and makes new offer Continue doing this until no more rejections of offers and then match Theorem: deferred acceptance where man proposes produces man-optimal stable matching (but woman gets worst possible matching) Theorem: man reports truthfully in deferred acceptance algorithm where man proposes but woman may have incentives to misrepresent preferences Theorem: There is no matching mechanism that produces truthful reporting of preferences and always generates stable outcomes given reported preferences. 7 / 18

8 One-to-one matching (marriage model) How to achieve stable matching? Thick (but search costs) 8 / 18

9 One-to-one matching (marriage model) How to achieve stable matching? Thick (but search costs) Uncongested 9 / 18

10 One-to-one matching (marriage model) How to achieve stable matching? Thick (but search costs) Uncongested Safe 10 / 18

11 One-to-one matching (marriage model) How to achieve stable matching? Thick (but search costs) Uncongested Safe 11 / 18

12 One-to-one matching (marriage model) How to achieve stable matching? Thick (but search costs) Uncongested Safe 12 / 18

13 One-to-one matching (marriage model) How to achieve stable matching? Thick (but search costs) Uncongested Safe 13 / 18

14 Efficient allocation? (Hitsch et al., 2010) Online dating sites can provide efficient matching (as predicted by the Gale-Shapley theorem) 14 / 18

15 Allocation mechanisms & design choices OkCupid doesn t really know what it s doing. Neither does any other website. (Christian Rudder, 2014) Allocation mechanisms: Decentralized matching Centralized clearinghouses/matchmaking (recommendations, restricted offer) Design choices: Verification/certification Signalling etc / 18

16 Signaling (Lee and Niederle, 2014) Evidence from Korean dating website: E-roses Users get limited (2 or 8) number of e-roses Users can send out (up to 10) date requests and attach e-rose More men use e-roses and send out more e-roses Roses sent proportionally to overall offers by desirability score Roses increase acceptance of date request by 20% effect driven by roses sent by more desirable people to less desirable people (no effect on most attractive people) 16 / 18

17 What have we learnt? Two-sided matching market not new Digitization affects all types of two-sided matching market Digitization leads to thicker markets and reduces frictions Online dating sites seem to produce efficient (stable) allocation But alternative allocation mechanisms may improve efficiency Other design choices could have large effect (e.g. signalling, verification) 17 / 18

18 References Gale, David and Lloyd Shapley (1962): College admissions and the stability of marriage, American Mathematical Monthly, Vol. 69, pp Hitsch, Günter, Ali Hortacsu, and Dan Ariely (2010): Matching and Sorting in Online Dating, American Economic Review, Vol. 100(1), pp Lee, Soohyung and Muriel Niederle (2014): Propose with a Rose? Signaling in Internet Dating Markets, Experimental Economics, forthcoming 18 / 18