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Conformity and Influence

  • David Goldbaum EMAIL logo
Published/Copyright: August 3, 2018

Abstract

I model the behavior of decision-makers seeking conformity and influence in a connected population. The model allows for one-sided linking, with information flowing from the target to the link’s originator. Conformity is achieved only with a social order, necessitating differentiated rewards despite ex ante homogeneity. The leader holds a strategic social location ex post, exerting influence independent of any leadership traits. A strong desire to influence produces non-conforming autonomous decision-makers. Socially detrimental multiple leaders can be sustained as well.

JEL Classification: C72; D83; D85

Acknowledgements

I am indebted to Buz Brock, Richard Carson, Sven Feldmann, Virginie Masson, William Schworm, Anne van den Nouweland, Joel Watson, John Wooders, and Myrna Wooders for their comments and suggestions, to AJ Bostian for continuing to engage with this project, and to seminar and conference participants at the University of Melbourne, George Washington University, University of New South Wales, University of California San Diego, the Australasian Economic Theory Workshop, and the anonymous reviewers. Support was provided by an industry linkage grant jointly financed by pureprofile Pty. Ltd. and the Australian Research Council (LP-0990750) and by the University of Technology Sydney Business Faculty Reader program.

Appendix

A Foundations

Formally, define

  1. h(i;g)={σ|iNL,NS(i;σ)} as the set of structures in which i leads;

  2. H(i;g)={σ|NL(σ)={i},NS(i;σ)=N{i}} as the set of structures in which i uniquely leads;

  3. hL(i,μis;g)={σh(i;g)|NL(σ)=NNS(i;σ)} as the set of structures in which i has μis followers and is the unique leader;

  4. h(iA,iB;g)={σh(iA;g)h(iB;g)} as the set of structures in which {iA,iB} are leaders;

  5. H(iA,iB;g)={σh(iA,iB;g)|NL(σ)={iA,iB}} as the set of structures in which only {iA,iB} lead and are leaders;

  6. Nc(i;a)={jN{i}|oi=oj} as, for action profile a, the set of conforming adopters;

  7. Ne(i;a)={jNc(i;a)|dj>di} as, for action profile a, the set of ensuing adopters;

  8. NS(i;σ)={jN|σji=1or σjj1==σjτi=1} as, for structure σ, the set of players who are successors to i;

  9. NL(σ)={jN|σjj=1} as, for structure σ, the set of players who lead;

  10. Nx(j;σ)={jxNS(i;σ)|dxidji} as, for structure σ, the set of players who are as close or closer to leader i as is j;

  11. Ny(j;σ)={jyNS(i;σ)NS(j,σ)|dyi>dji} as, for structure σ, the set of players who are farther from leader i than is j but not successor to j;

  12. Nα(jh;σ)={jαNS(ih;σ)|djαihdjhih} as, for structure σ, the set of players who are as close or closer to leader ih as is j to ih;

  13. Nβ(jh;σ)={jβNS(ih;σ)NS(j,σ)|djβih>djhih} as, for structure σ, the set of players who are farther from leader ih than is j to ih;

  14. NAB(iA,iB;σ)={j|Nd(j;g){iA,NS(iA;σ)},Nd(j;g)(iB,NS(iB;σ)}} as, for structure σ, the set of players with potential links to members of both of the iA-led tree and the iB-led tree;

and recognize that for gGn, Nd(ih;g){NS(ih;σ),ih}= implies NAB(iA,iB;σ)NS(ih;σ).

An () on the set of structures indicates that all followers imitate the contact offering the shortest distance to the leader, that is, aj=argminNd(j;g)djijNs(i;σ). The sets hL(i;g) and h(iA,iB;g) have the additional condition that the Nl(σ) population is at least as distant from the leader as is the most distant follower, measured on g, dijdijˉ(μihs) for jNl(σ), h=,A,B.

Utility of Interactions

Individuals face a discrete choice in which they receive utility from the interaction between their own choice and the choices of other members in the population. Let the m×dˉ matrix ωi denote the adoption of an option with element wi,o,d=1 if player i adopts option oiO at distance di=d. Otherwise, ωi,o,d=0. Let ωi=(ω1,,ωi1,ωi+1,,ωn) represent the choices of all agents other than i. Individual utility can be defined broadly as the sum of three elements:

V(ωi)=u(ωi)+S(ωi,ωi)+ϵ(ωi).

The current analysis considers only the social utility associated with a choice, S(ωi,ωi), setting the innate preferences over the different options, u(ωi), and the idiosyncratic random element of utility, ϵ(ωi), each to zero.[24]

Let the n×dˉ matrix Ωi denote the possession of an option with element Ωi,o,d=1 when player i adopts option oiO at distance did. Otherwise, Ωi,o,d=0. Let

μi=jiωj

and

νi=jiΩj

so that μi denotes the aggregate choice for each option over all distances and νi denotes the cumulative aggregate choice at each distance.

The complementarities of the social choice depend only on the two measures of popularity,

μic=1μiωi1

and

μie=μic1ωiνiωi1.

Let

S(ωi,μic,μie)=ϕ(μic)+ψ(μie),

then linearity with ϕ(x)=rcx and ψ(x)=rex produces constant cross partials

2S(ωi,uic,μie)ωi,o,dμi,o,d=rcand 2S(ωi,uic,μie)ωi,o,dνi,o,d=re,i,o,d

so that dependence across players is captured by the two constant coefficients.

B Propositions, Lemmas, and Proofs

Formal Statement and Proof of Proposition 1 and Corollary 1

Proposition 1.

For σHi;g, σh(i,σ;g), and λ(μ)0, then πNL(j,σ)πNL(j,σ) for all jNS(i;σ) if and only if BNL0.

Proof.

Let σj indicate the strategies of all players in Nj. For σH(i;g), let σ=σj×σj and σjj=1 producing σh(i,σ;g). Let μjh=μh(j;σ)=|Nh(j;σ)| for h=x,y,s so that relational populations are identified according to the structure σ. Recall ϕ(μ)>0 and ψ(μ)>0. For player jNS(i;σ),

π(j;σ)=ϕ(n1)+ψ(μjy+μjs).

When leading, uncertainty in the outcome of whether oi=oj generates uncertainty in j’s payoff. Expectations are taken over the possible realization of oi and oj with

(21)E(π(j;σ))=1m(ϕ(n1)+ψ(μjx+μjy+μjs))+m1m(ϕ(μjs)+ψ(μjs)).

The condition ANL(j;σ)0, derived from E(π(j;σ)π(j;σ))0, ensures that player jNS(i;σ) prefers her position as a follower of i over leading.

The condition BNL0 is equivalent to A(jˉ;σ)0 for jˉ=argmaxjNS(i;σ)dji. For jˉ, μy(jˉ)=μs(jˉ)=0, leaving ANL(jˉ;σ)=((m1)/m)ϕ(n1)(1/m)ψ(n2)0, or

ANL(jˉ;σ)=A1+A3(0)0.

The first term is strictly positive. BNL0 implies A3(0)A1. For follower j, ANL(j;σ) is as defined in eq. (5).

That A3(μ)A1 for all μ[0,n2] is a necessary and sufficient condition for A(j;σ)0 for all j. Given A(jˉ;σ)0, a sufficient condition is that A3(μ) remain everywhere above a monotonic function passing through A3(0) and A3(n2). Observe,

A3(μ)=1m(m1)ϕ(n2)ψ(n2)(m1)ϕ(μ)ψ(μ)

and

A3(0)=1m(m1)ϕ(n2)ψ(n2).

Since λ(μ)0 implies

ϕ(μ)ψ(μ)ϕ(n2)ψ(n2),

for λμ<0,

A3(μ)=A3(0)(m1)ϕ(μ)ψ(μ)=A3(0)(m1)ϕ(μ)ψ(μ)1ψ(μ)>A3(0)(m1)ϕ(n2)ψ(n2)1ψ(μ)=A30(μ).

A30(μ) is an afine transformation of ψμ

A30(μ)=A3(0)(m1)ϕ(n2)ψ(n2))ψ(μ)ψ(n2)=A3(0)A3(0)ψ(μ)ψ(n2)=A3(0)1ψ(μ)ψ(n2).

Corollary 1.

For σHi;g, σ={h(i,σ;g)|σjj=1}, and BNL0, if λμ<0 then πNL(j,σ)<πNL(j,σ) is possible for some jNS(i;σ){jˉ}.

Proof.

For λμ<0 so that

ϕ(μ)ψ(μ)>ϕ(n2)ψ(n2),

then A3(μ)<A30(μ). While A3(μ)A1 remains possible, it is no longer assured by A3(0)A1.

Evaluation of Proposition 1 with Linear Payoff

Proof.

Let σj indicate the strategies of all players in Nj. For σHi;g, let σ=σj×σj and σjj=1 producing σh(i,σ;g). Let μjh=μh(j;σ)=|Nh(j;σ)| for h=x,y,s. For player jNi,

(22)E(π(j;σ))=rc(μjx+μjy+μjs+1)+re(μjy+μjs).

The payoff to j when leading is uncertain due to the uncertainty in the outcome of whether oi=oj.

(23)E(π(j;σ))=1m((rc+re)(μjx+μjy+μjs)+rc)+m1m(rc+re)μjs
=(rc+re)μjs+1m((rc+re)(μjx+μjy)+rc).

The condition A(j;σ)0, derived from E(π(j;σ)π(j;σ))0, ensures that player jNS(i;σ) prefers her position as a follower of i over leading.

The first term of Aj;σ as expressed in eq. (8) is strictly positive. The coefficient on the second term is also positive. For θ=(m1)rc/re>1 the third coefficient is also positive making it a sufficient condition for Aj;σ>0 for all jNi. The necessary and sufficient condition ensuring A(j;σ)0 for all jNi sets a lower threshold on θ, allowing the third term to be negative. For

(m1)rc((m1)rcre)(n2)

or equivalently, θ11n1, Aj;σ>0 for all j since μx(j)μx(jˉ)=n2 and μy(j)μy(jˉ)=0.

Formal Statement of Lemma 1

Lemma 1.

Forσ,σHi;g with σj=σjand{aj,aj}Nd(j;g), then forμx(j;σ)μx(j;σ),

π(j;σ)=π(j;σ)if μx(j;σ)=μx(j;σ),pi(j;σ)if μx(j;σ)<μx(j;σ).

Formal Statement and Proof of Lemma 2

Lemma 2.

σhi;g is a necessary condition for σhi;gto be a Nash equilibrium.

Proof.

For player i, leading dominates following since to choose one’s own successor as a predecessor pays zero. From μe(j)=μjy+μjs and μjy+μjs=μisμjx, decreasing μjx increases πNL(j;σ) for any reward function that is increasing in μe. Among the following options, a player can do no better than to minimize μjx. A player who is not minimizing μjx is not optimizing against her available following options. Thus, any structure σhi;ghi;g cannot be a Nash equilibrium.

The BNL0 application of Lemma 2 is to σHi;g. For σHi;g each player is optimizing from the set of strategies that preserve σHi;g. Minimizing μjx is also a necessary attribute of hL(i,nˉ;g) for optimizing behavior under BNL<0.

Formal Statement and Proof of Proposition 2

Proposition 2.

Given λ(μ)0, {H(i;g)}iN a set of equilibrium structures if and only if B0.

Proof.

From Proposition 1, given BNL0, every player jNi prefers any structure σHi;g over the structure produced by player j’s deviation to lead. In combination with Lemma 2, BNL0 implies that no follower in the population can do better for herself than to minimize her μjx.

Corollary 2.

Given λ(μ)0, {H(i;g)}iN is a set of equilibrium structures if and only if BNL0.

Proof

H(i;g)H(i;g) implies that for σHi;g and ajNd(j;g), if aj=argminNd(j;g)dji, then aj=argminNd(j;g)μjx. As further distinction between the strategies, structure σHi;g if σH(i;g) or if σ=σj×σj with aj=j where σHi;g and where jNS(i;σ) satisfies the following three properties:

  1. There exists jNd(j,g) with dji=dji, indicating that j′ is equidistant to the leader as is j and that j has the option to imitate j′,

  2. μy(j;σ)=0, indicating that there are no successors to i of greater distance to i than j without also being a successor to j, and

  3. either μs(j;σ)=0 or μs(j;σ)>0 with successors NS(j;σ) having no option to link to i but through j.

For {j1,j2}Nd(j;g) with dj1i<dj2i, let σh=σ|σjjh=1, h=1,2, so that μx(j,σ1)μx(j,σ2). The condition that allows μx(j,σ1)=μx(j,σ2) is μjy=0. With j2NS(j;σ), μjy=0 implies j2Nx(j;σ) and dj2i=dji=dj1i+1. For σ1H(i;g), a necessary and sufficient condition to have σ2H(i;g) is that for all jsNS(j;σ1), Nd(js;g){NS(j;σ){j}}. The condition establishes that no successor of j has the option to link to i without having the chain of links pass through j, a condition necessary to ensure that μx(js;σ2) is minimized for all js.

From H(i;g)H(i;g), σH(i;g) is an equilibrium if BNL0 and σH(i;g) is not an equilibrium if BNL<0.

Formal Statement and Proof of Proposition 3

Proposition 3.

For B<0 and σhL(i,μis;g), all jNS(i;σ) prefer following i to leading and the remaining population, jN{i,NS(i;σ)}, prefer leading to following i if and only if σhL(i,nˉ;g).

Proof.

Let μjh=μh(j;σ)=|Nh(j;σ)| for h=x,y,s and μl=μl(σ)=|NL(σ)|. For σhL(i,μis;g), let σ=σj×σj and σjj=1, jNS(i;σ). Uncertainty in the payoff to j when following stems from the uncertainty in whether oi=ol for each lNL(σ){i} with,

(24)E(π(j;σ))=rc(μjx+μjy+μjs+1)+re(μjy+μjs)+1mrc(μl1).

The payoff to j when leading is uncertain due to the uncertainty in the outcome of whether oj=ol for each lNL(σ) with,

(25)E(π(j;σ))=(rc+re)μjs+1m(rc+re)(μjx+μjy)+1mrc(μl(σ)).

E(π(j;σ)π(j;σ)) from eqs. (24) and (25) is the same as from eqs. (22) and (23) when expressed in terms of μjx, μjy, and μjs as in eq. (8).[25] The presence of a population of autonomous adopters does not alter the condition A(j;σ)0 for player j to prefer following to leading. Let jˉ(μis)=argmaxjNS(i;σ)dji, then μy(jˉ(μis))=μs(jˉ(μis))=0 and μx(jˉ(μis))=μis1 so that

(26)A(jˉ(μis);σ)=1m((m1)rc+((m1)rcre)(μis1))

and C(μis;θ)=A(jˉ(μis);σ)m/reμis. With B<0, ((m1)rcre)<0 so that A(jˉ(μis);σ) decreases as the size of the tree increases. For μis=1, A(jˉ(1),σ)=(m1)rc>0 while B<0 means that for μis=n1, A(jˉ(n1);σ)<0.

For m=1, A(j;σ)=reμjx<0. For rc=0, A(j;σ)=re((m1)μjyμjx) so that the most distant follower, with A(jˉ(μis);σ)=re(μis1)0, prefers to lead in the presence of other followers. Player jˉ is indifferent to leading only when she is the only follower, m=1, and rc=0. With a non-trivial choice m<1 and a preference for conformity (rc>0), the equilibrium structure requires μis1.

The value of μis that sets C(μis;θ)=0 need not be an integer. There exists nˉ{floor(μ),ceil(μ)} such that A(j(nˉ);σ)0 and A(j(nˉ+1);σ)<0. A structure σhL(i,nˉ;g)hL(i,nˉ;g) cannot be an equilibrium because either there are members of NS(i;σ) able to improve their payoff by choosing a different predecessor offering a shorter distance to i or there is a member of NL(σ) able to improve her payoff by choosing to follow a predecessor offering a shorter distance to i than the current jˉ(μis) player. For σhL(i,nˉ;g), no player is able to improve her payoff through unilateral deviation while preserving a single-leader structure.

To extend Proposition 3 to the nonlinear reward setting of eq. (1), let ANLl represent the expected payoff differential for following over leading in the presence of a non-empty autonomous NL(σ){i} population. Then

ANLl(j;σ)=ANL(j;σ)+A4(μjs).

A4(μjs) is a term capturing the net following over leading expected contributions of the NL(σ) population for follower j. Because expectations are being taken over nonlinear functions, each possible outcome requires a separate term in a large NL(σ) population. As a simple illustration, consider a single autonomous adopter so that μl=2. Then,

(27)A4(μjs)=m1m2((ϕ(μis+1)ϕ(μis))(ϕ(μjs+1)ϕ(μjs))).

Given leader i and follower j, let l identify the autonomous agent. The first inner parenthetical term of eq. (27) captures the value to j of matching with l when already adopting the same alternative as i, either as a follower of i or as a leader having also matched with i. The second inner parenthetical term is the value to j of matching with l when not adopting the same alternative as i. Here, and in general with μl3 as well, A4(μjs) is positive and decreasing in μjs for ϕ′′(μ)>0, zero for ϕ′′(μ)=0, and negative and increasing for ϕ′′(μ)<0. The condition ϕ′′(μ)0 ensures that jˉ remains the marginal decision-maker since A4(μjs) is at its maximum at μjs=0.

Formal Statement and Proof of Proposition 4

Proposition 4.

{hL(i,n;g)}iNis the set of equilibrium strategies if and only if B < 0.

Proof.

Let μhs=μs(ih). For σh(iA,iB;g), let σ=σj×σj, with σjj=1, jNS(i;σ). For j, the expected payoff for following and leading are, respectively,

(28)E(π(j;σ))=rc(1+μjx+μjy+μjs)+re(μjy+μjs)
+1m(rc(μl(σ)1+μjα+μjβ)+re(μjβ)),
(29)E(π(j;σ))=(rc+re)μjs+1m((rc+re)(μjx+μjy+μjα+μjβ)+rcμl(σ))

where μjh=μh(j;σ)=|Nh(j;σ)| for h=x,y,s,α,β and μl=μl(σ)=|NL(σ)|. Observe, for h=A,B,

1+μjx+μjy+μjs=μhs+μjα+μjβ=μhs+μl=n.

The condition E(π(j;σ)π(j;σ))0 implies D(jh;σ)0 as reported in eq. (12). For the most distant player(s) from ih according to σ, E(ih;σ)=D(jˉ(μhs);σ)/reμhs. With μy(jˉ(μhs))=μs(jˉ(μhs))=0, μx(jˉ(μhs))=μhs1, and μα(jˉ(μhs))μα(jh) for all jhNS(ih;σ), D(jˉ(μhs);σ)0 implies D(jh;σ)0 for all jNS(ih;σ), so that E(ih;σ)0 is necessary and sufficient to ensure D(jh;σ)0 holds for all jhNS(ih;σ). Since

1+μhα(jμhs)1μhs1>11n1,

the condition E(ih;σ)0 violates B>0. For σhL(i,nˉ;g), no player is able to improve her payoff through unilateral deviation.

To extend Proposition 4 to the nonlinear reward setting of eq. (1), let DNL represent the expected payoff differential for following over leading in the presence of a two leaders, iA and iB. As reference for jhNS(ih;σ), let σhL(i;g) have a tree under i that matches the tree structure under ih according to σ and where all non-members of NS(ih;σ) adopt autonomously (rather than following ih). For follower jh,

DNL(j;σ)=ANL(j;σ)+m1m2D1+1m2D2D1=ϕ(μhs+μhs+1)ϕ(μhs)(ϕ(μjs+μhs+1)ϕ(μjs))D2=ψ(μjy+μjz+μjβ)ψ(μjy+μjz){m1m(ψ(μjz+μhs)ψ(μjz))+1m(ψ(μhs+μhs1)ψ(μhs1))}

For j considering whether to lead or follow, D1 is the conformity contribution of joining the ih-led hierarchy when already affiliated with the ih-led hierarchy (as a follower or by independently matching) less conformity contribution of joining the ih-led hierarchy when not affiliated with the ih-led hierarchy. D2 is the ensuing contribution of matching with the ih-led hierarchy as a follower of ih less the expected ensuing contribution of matching with the ih-led hierarchy when leading (made up of matching with just ih and matching with both ih and ih). jˉ(μhs) remains the marginal decision-maker in the ih-led hierarchy for λ(μ)0 and ϕ′′(μ)0 (conditions that combined to also require ψ′′(μ)0).

If linearized, DNL(j;σ) collapses to A(j;σ)reμjα/m=D(j;σ).

Formal Statement and Proof of Proposition 5

Proposition 5.

For

H+(iA,iB;g)={σH(iA,iB;g)}such thatNd(ih;g){NS(ih;σ),ih}=,E(ih,μhs,θ,σ)0,Fh(jh;θ,m,σ)0 for all jNAB(iA,iB;σ),

a structure σH(iA,iB;g) is a Nash equilibrium if and only if σH+(iA,iB;g). The set H+(iA,iB;g) is feasibly non-empty.

Proof.

For σH(iA,iB;g), without loss of generality, let μAsμBs. With gGn, {ihNAB(iA,iB;σ)}{ihNS(ih;σ)}, h=A,B are both nonempty sets. The compliments, {ih,NS(ih;σ)}NAB(iA,iB,σ) for h=A,B, can be nonempty, indicating that possibly ih and some jNS(ih;σ) have no direct potential link to {ih,NS(ih;σ)} with the current σ.

For player jhNS(ih;σ), the expected payoff for remaining a follower in the ih-led tree is E(πh(j;σ)) as expressed in eq. (28). Let σhh=σjh×σjh, with jhNS(ih;σhh). That is, σhhH(iA,iB;g) represents the alternative to σH(iA,iB;g) based on a switch by player jhNAB(iA,iB;σ)NS(ih;σ) from the ih-led tree to the ih-led tree. Compute

E(π(jh;σ)π(jh;σhh))=1m((m1)rc(μhsμhs1μs(jh))+re(μβ(jh)μhβ(jh)+m(μy(jh)μhy(jh))).

The condition FA0 of eq. (15) corresponds to E(π(jA;σ)π(jA;σAB))0 and the condition FB0 of eq. (16) corresponds to E(π(jB;σ)π(jB;σBA))0.

For leader ih, the condition Fh(ih)0 reduces to

θ11μhs+10.

Since μhs(n2), B0 ensures that Fh(ih)0 for both leaders. The condition holds at equality only if B=0 and μhs=(n2), a condition that cannot hold for both leaders simultaneously. Fh(j)>0 for all jNAB(iA,iB;σ) is feasible.

C Examples

Multiple-Leader Structures

Two scenarios allow for a multiple leader structure in equilibrium with linear payoff functions. Both feature a σ given gGn such that a particular follower finds it advantageous and feasible to preserve the multiple leader structure.

Example 4

Let μh(j)=μh(j;σ)=|Nh(j;σ)| for h=x,y,s,α,β. For h=A,B, let σ=σjh×σjh be the structure produced by jh switching predecessors in order to become a member of the ih-led tree. The alternative structure identifies populations Nhβ(jh)=Nβ(jh;σ) and Nhy(jh)=Ny(jh;σ). Let μhβ(jh)=|Nhβ(jh)| and μhy(jh)=|Nhy(jh)|.

The structure σ is as depicted in Figure 9. With μy(jA)=μs(jA)=μβ(jA)=μy(jB)=μs(jB)=μAβ(jB)=0, FA0 and FB>0 of eqs. (17) and (18) jointly imply

(30)μBβ(jA)θ+1dμ<μβ(jB)mμAy(jB)θ1.

The four key features needed of σ to satisfy eq. (30) are

  1. μBs1+μα(jB)+(mμAy(jB)(θ1)μβ(jB))/θ indicating that μBs is larger than μAs excluding the NS(iA;σ) followers at distance djB,iB+1. Each member of the NAy(jB) population requires m members of NS(iB;σ) to keep jB in NS(iB;σ). θ=1 is the minimum possible threshold on θ derived from Eh0. The stronger condition μBs1+μα(jB)+mμAy(jB) ensures FB0 over the entire feasible support for θ;

  2. a concentration of the iA-led population at the distance djB,iB+1 is sufficiently large to have μAsμBs despite feature 1;

  3. djB,iAdjB,iB+1; and

  4. djA,iB=djB,iB+1.

Figure 9 is an equilibrium structure satisfying eq. (30). Feature 1 requires a large Nx(jB;σ) population based on the sizes of the Nα(jB;σ) and NAy(jB;σ) populations. The Nβ(jB;σ) population is sufficiently large to produce μAsμBs in accordance with feature 2. So that jB prefers the iB-led tree, she cannot benefit from the Nβ(jB;σ) population were she to switch, which is captured by feature 3. Feature 4 puts jA in a position where she fails to share in jB’s distance advantage over the β population from the iB-led tree, thereby keeping μBβ(jA) small. By feature 3, the β population exists within the distance range djB,iB+1 and djB,iA (inclusive) but feature 4 constrains the population to have a distance of djB,iB+1.

Example 5

The inequality FB(jB)>0 supports follower jB{NS(iB,σ)NAB(iA,iB;σ)|μjs=0,μjy>0} in her current position, as illustrated in Example 5. The additional imposition of μAy(jB)=0 minimizes the attraction of the iA-led tree to jB as it implies player jB must join the iA-led tree at the maximum distance.

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Published Online: 2018-08-03

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