Addendum to

Equilibria under Deferred Acceptance: Dropping Strategies, Filled Positions, and Welfare Paula Jaramillo∗ Çaˇgatay Kayı† and Flip Klijn‡ June 2, 2016 We refer to Jaramillo et al. (2014) for the model and the notation. Example 3 in Jaramillo et al. (2014), which is reproduced below, exhibits a many–to–one market for which there exists an equilibrium outcome such that simultaneously for each side of the market there is an agent that is strictly worse off and another agent that is strictly better off than in any stable matching. After the publication of Jaramillo et al. (2014), we have employed exhaustive computer calculations1 to find the full set of matchings that are obtained in Nash equilibria. Below we report on our findings. Example 1. [Jaramillo et al., 2014, Example 3] Consider a many–to–one market (PS , H ) with 6 students, 3 hospitals, and preferences over individual partners P given by the columns in Table 1. The hospitals have quota 2. Moreover, assume that {s1 , s4 } h2 {s5 , s6 }. Note that this assumption does not contradict the responsiveness of h2 ’s preferences. What we knew: One easily verifies that the student-optimal stable matching µ := ϕS (P ) is given by ∗

Corresponding author. Universidad de los Andes, Facultad de Economía, Calle 19A # 11 - 37, Bloque W, Bogotá, Colombia; e-mail: [email protected] † Universidad del Rosario, Bogotá, Colombia. Ç. Kayı gratefully acknowledges the hospitality of Institute for Economic Analysis (CSIC) and financial support from Colciencias/CSIC (Convocatoria No: 506/2010), El Patrimonio Autónomo Fondo Nacional de Financiamiento para la Ciencia, la Tecnología y la Innovación, Francisco José de Caldas. ‡ Institute for Economic Analysis (CSIC) and Barcelona GSE, Spain. F. Klijn gratefully acknowledges the financial support from CSIC/Colciencias 2010CO0013 and and the Spanish Ministry of Economy and Competitiveness through Plan Nacional I+D+i (ECO2011–29847) and the Severo Ochoa Programme for Centres of Excellence in R&D (SEV-2011-0075). 1 The programs implemented in Matlab are available upon request.

1

s1

s2

Students s3 s4

h2

h∗1

h1

h3

h∗2

h2

s∗1

s∗4

s5

h∗3

h∗2

h3

h∗3

s∗2

s∗5

s∗6

s3

s6

s∗3

s1

s4

h∗1

s5

s6

Hospitals h1 h2 h3

Table 1: Preferences P in Example 1

h1 h2 h3 µ: | | | {s1 , s2 } {s5 , s6 } {s3 , s4 } which is the boxed matching in Table 1. The only other stable matching in Σ(P ) is given by h1 h2 h3 µ : | | | {s1 , s2 } {s4 , s5 } {s3 , s6 } ∗

which is the matching marked with ∗ in Table 1. The difference between µ and µ∗ is in the matches of students s4 and s6 . Consider the dropping strategies Q0h1 = s2 , s3 , Q0h2 = s4 , s1 , and Q0h3 = s5 , s6 , s4 for hospitals h1 , h2 , and h3 . It is easy to verify that Q0 = (Q0h1 , Q0h2 , Q0h3 ) is an equilibrium and that it induces the matching h1 h2 h3 0 S 0 µ := ϕ (Q ) : | | | {s2 , s3 } {s1 , s4 } {s5 , s6 }. which is the boldfaced matching in Table 1. At the unstable equilibrium outcome µ0 , for each side of the market there is an agent that is strictly worse off than in any stable matching and there is another agent that is strictly better off than in any stable matching. Indeed, for the hospitals’ side we observe that µ(h1 ) = µ∗ (h1 ) h1 µ0 (h1 ), but µ0 (h3 ) h3 µ∗ (h3 ) h3 µ(h3 ). And similarly, for the students’ side we find that µ(s5 ) = µ∗ (s5 ) Ps5 µ0 (s5 ), but µ0 (s1 ) Ps1 µ(s1 ) = µ∗ (s1 ). What we now know as well: Consider the dropping strategies Q00h1 = s1 , s2 , Q00h2 = s4 , s6 , and Q00h3 = s5 , s3 , s4 for hospitals h1 , h2 , and h3 . It is easy to verify that Q00 = (Q00h1 , Q00h2 , Q00h3 ) is an equilibrium and that it induces the matching h1 h2 h3 00 S 00 µ := ϕ (Q ) : | | | {s1 , s2 } {s4 , s6 } {s3 , s5 }. 2

which is the underlined matching in Table 1. Matching µ00 does not exhibit the same welfare features as µ0 , i.e., it is not true that at the unstable equilibrium outcome µ00 , for each side of the market there is an agent that is strictly worse off than in any stable matching and there is another agent that is strictly better off than in any stable matching. (To see this note that at µ00 there is no student that is strictly better off than at any stable matching. Note also that at µ00 there is no school that is strictly worse off than at any stable matching.) Exhaustive computer calculations show that there is no other matching that can be sustained at a Nash equilibrium. Hence, O(H ) = {µ, µ∗ , µ0 , µ00 }. Since Q0 and Q00 consist of dropping strategies and since µ and µ∗ can be obtained in some equilibria that consist of dropping strategies (Jaramillo et al., 2014, Proposition 1), all equilibrium outcomes can be obtained in equilibria that consist of dropping strategies. 

References [1] P. Jaramillo, Ç. Kayı, and F. Klijn (2014). Equilibria under Deferred Acceptance: Dropping Strategies, Filled Positions, and Welfare Games and Economic Behavior, 82(1), 693-701.

3

Equilibria under Deferred Acceptance: Dropping ...

Jun 2, 2016 - s3 s4 s5 s6 h1 h2 h3 h2 h∗. 1 h1 h3 h∗. 2 h2 s∗. 1 s∗. 4 s5 h∗. 1 h∗. 3 h∗. 2 h3 h∗. 3 s∗. 2 s∗. 5 s∗. 6 s3 s6 s∗. 3 s1 s4. Table 1: Preferences ...

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