Abstract
We propose the notion of minimal farsighted instability to determine the states that are more likely to emerge in the long run when agents are farsighted. A state is minimally farsighted unstable if there is no other state which is more farsightedly stable. To formulate what it means to be more farsightedly stable, we compare states by comparing (in the set inclusion or cardinal sense) their sets of farsighted defeating states. We next compare states in terms of their absorbtiveness by comparing both their sets of farsighted defeating states (i.e. in terms of their stability) and their sets of farsighted defeated states (i.e. in terms of their reachability). A state is maximally farsighted absorbing if there is no other state which is more farsightedly absorbing. We provide general results for characterizing minimally farsighted unstable states and maximally farsighted absorbing states, and we study their relationships with alternative notions of farsightedness. Finally, we use experimental data to show the relevance of the new solution concepts.
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Notes
Jackson (2008) defines the notion of farsightedly pairwise stable network: a network is a farsightedly pairwise stable network if and only if it is not farsightedly defeated.
The set of farsighted defeating states of a given state is the set of all states that farsightedly defeat this state.
The existence of a farsighted improving path means that some agents could leave the current state anticipating further deviations from other agents leading to a final state, where all the agents deviating along the path are better off than at the current state when they deviate. Thus, it is a potential source of instability. And the higher the number of farsighted improving paths emanating from a state, the higher the potential instability of such state.
The set of farsighted defeated states of a given state is the set of all states that this state farsightedly defeats.
Alternative notions of farsightedness are suggested by Bloch and van den Nouweland (2020); Diamantoudi and Xue (2003); Dutta et al. (2005); Dutta and Vohra (2017); Ray and Vohra (2019); Herings et al. (2004); Kimya (2020); Mauleon and Vannetelbosch (2004); Page and Wooders (2009); Xue (1998) among others.
Throughout the paper we use the notation \(\subseteq \) for weak inclusion and \(\varsubsetneq \) for strict inclusion. Finally, \(\#\) will refer to the notion of cardinality.
Mauleon and Vannetelbosch (2016) provide a comprehensive overview of the solution concepts for solving network formation games.
The notion of farsighted improving path is introduced by Jackson (2008) and Herings et al. (2009) in network formation, Herings et al. (2010) in coalition formation, and Mauleon et al. (2022) in normal-form games. Jackson and Watts (2002) define the notion of improving path in network formation when all players are myopic. Herings et al. (2020); Luo et al. (2021) and Mauleon et al. (2022) extend this notion to a mixed population composed of both myopic and farsighted players in matching markets, network formation problems and coordination games, respectively.
Diamantoudi and Xue (2003) study the farsighted core in hedonic games.
Mauleon et al. (2022) show that there always exists a farsightedly stable strategy profile in coordination games but, in general, there is no guarantee that there exist farsightedly stable strategy profiles.
Take \(b(1)=3\), \(b(2)=1.5\), \(b(3)=0.5\), \(c=2\) and \(n=4\). Computing the number of defeating networks (\(\#\phi (g)\)) and number of defeated networks (\(\#\phi ^{-1}(g))\)) for each network, we obtain that \(F_{\#}= \{g \in {\mathcal {G}} \mid g \text { is a 2-regular network}\}\) while \(A_{\#} = \{g \in {\mathcal {G}} \mid g \text { is a star network}\} \cup \{g \in {\mathcal {G}} \mid g \text { is a 2-regular network}\}\).
See Luo et al. (2021).
The design of the termination rule allows each individual to decide unilaterally to continue playing (at least for the first 26 stages).
Since the empty network is pairwise stable in all treatments, myopia predicts the empty network in T1, T2 and T3.
From the experimental data, we only have the number of groups ending in each class in the experiment. The average number of groups ending in any given network is then equal to the number of groups ending in each class divided by the number of different networks in the class.
There are four different networks in class \(C_{5}\). In total 53.6% of the groups end up in class \(C_{5}\).
In the column T3* the percentage is computed considering the number of payoff relevant networks in each class. For instance, the number of groups ending on average in a representative network of class \(C_{7}\) becomes 3% (instead of 1.5% in Table 3) since there are only 6 different payoff relevant networks in this class.
With respect to the first criteria, FSS is (weakly) dominated by F, and with respect to the second criteria, FSS is (weakly) dominated by \(F_{\#}\).
When we separate the classes of networks between those with a frequency higher than \(1\%\) and the others, F (and \(A_{\#}\)) has the highest correlation with this binary criteria.
Notice that the vNM farsighted stable set is also based on the notion of farsighted improving path, given by the correspondence \(\phi (x)\), that ignores the possibility that one might avoid enforcing a new state that is valuable to her anticipating further changes making her worse off. This drawback was already pointed out by Chwe (1994) and partially solved by the largest consistent set and the DEM farsighted stable set.
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Acknowledgements
Ana Mauleon and Vincent Vannetelbosch are, respectively, Research Director and Senior Research Associate of the National Fund for Scientific Research (FNRS). Financial support from the Fonds de la Recherche Scientifique - FNRS research grant T.0143.18 is gratefully acknowledged.
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Appendix
Appendix
1.1 Experimental data
Let \(g^{i}\) be a generic network in class \(C_{i}\) and \(c_{i}\subseteq C_{i}\) a generic proper subset of the corresponding class. We will write \( g^{i}\rightarrow g\) with \(g\in C_{j}\), and \(g^{i}\rightarrow g\) with \(g\in c_{j}\), when the generic network \(g^{i}\) in class \(C_{i}\) reaches with a farsighted improving path all the networks in class \(C_{j}\) or only a proper subset \(c_{j}\) of \(C_{j}\), respectively.
1.1.1 Minimally unstable and maximally absorbing networks in T1
In treatment 1 (T1) the set of networks that can be reached from \(g^{i}\) by some farsighted improving path are:
Remember that a network \(g\in {\mathcal {G}}\) is minimally farsighted unstable if there is no \(g^{\prime }\ne g\) such that \(g^{\prime }\gnsim ^{\sigma (\subseteq )}g\); i.e., \(\phi (g^{\prime })\varsubsetneq \phi (g)\). It follows that \(g^{N}\) is the unique minimally farsighted unstable network. Notice that \(g^{\emptyset }\) is more farsightedly stable than all networks \(g\ne g^{10},g^{N}\).
A network \(g\in {\mathcal {G}}\) is maximally farsighted absorbing if there is no \(g^{\prime }\ne g\) such that \(g^{\prime }\gnsim ^{\alpha (\subseteq )}g\); i.e., \(\phi (g^{\prime })\subseteq \phi (g)\) and \(\phi ^{-1}(g)\subseteq \phi ^{-1}(g^{\prime })\) with one inclusion holding strictly. Since \(\phi (g^{N})=\emptyset \) and \(g^{N}\in \phi (g^{i})\) for all \(g^{i}\ne g^{N}\), it also holds that \(g^{N}\) is the unique maximally farsighted absorbing network.
A network \(g\in {\mathcal {G}}\) is \(\#\)-minimally farsighted unstable if there is no \(g^{\prime }\ne g\) such that \(g^{\prime }\gnsim ^{\sigma (\#)}g\); i.e., \(\#\phi (g^{\prime }) < \#\phi (g)\). Thus, \(g^{N}\) is the \(\#\)-minimally farsighted unstable network.
A network \(g\in {\mathcal {G}}\) is \(\#\)-maximally farsighted absorbing if there is no \(g^{\prime }\ne g\) such that \(g^{\prime }\gnsim ^{\alpha (\#)}g\); i.e., \(\#\phi (g^{\prime })\le \#\phi (g)\) and \(\#\phi ^{-1}(g)\le \#\phi ^{-1}(g^{\prime })\) with one inequality holding strictly. Then, \(g^{N}\) is the \(\#\)-maximally farsighted absorbing network.
1.1.2 Minimally unstable and maximally absorbing networks in T2
Let us denote \(B_{g}\) the networks that are adjacent to g,
and let \({\overline{B}}_{g}\) be its complement. In treatment 2 (T2) the set of networks that can be reached from \(g^{i}\) by some farsighted improving path are:
Note that \(g^{\emptyset }\) is more farsightedly stable than all networks \(g\ne g^{5},g^{9}\). Moreover, neither \(g^{5}\) nor \(g^{9}\) are more farsightedly stable than \(g^{\emptyset }\). Thus, \(g^{\emptyset }\) is minimally farsighted unstable. Note also that it is not always true that \((C_{9}{\setminus } g^{9})\supseteq (C_{9}\cap {\overline{B}}_{g^{5}})\) since \(g^{9}\) could belong to \((C_{9}\cap {\overline{B}}_{g^{5}})\). Then, also \(g^{5},g^{9}\) are minimally farsighted unstable.
The maximally farsighted absorbing networks are \(g^{\emptyset },g^{5}\) and \(g^{9}\). Indeed:
and
From Table 8, we have that \(g^{\emptyset }\) is the \(\#\)-minimally farsighted unstable network. Notice that the network \(g^{\emptyset }\) is \(\#\)-more farsightedly absorbing than the network \(g^{2}\), but not more than \(g^{5}\) and \(g^{9}\).
1.1.3 Minimally unstable and maximally absorbing networks in T3
In treatment 3 the set of networks that can be reached from \(g^{i}\) by some farsighted improving path are:
Note that \(g^{\emptyset }\) is more farsightedly stable than all networks \(g\ne g^{7}\). Moreover, \(g^{7}\) is not more farsightedly stable than \(g^{\emptyset }\). Thus, \(g^{\emptyset }\) and each \(g^{7}\) are minimally farsighted unstable networks. The maximally farsighted absorbing networks are also \(g^{\emptyset },g^{7}\) and \(g^{10}\). Indeed:
and
From Table 8, we have that \(g^{\emptyset }\) is the \(\#\)-minimally farsighted unstable network. Remark that the network \(g^{7}\) is \(\#\)-more farsightedly absorbing than all other networks except \(g^{\emptyset }\).
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de Callataÿ, P., Mauleon, A. & Vannetelbosch, V. Minimal farsighted instability. Int J Game Theory (2024). https://doi.org/10.1007/s00182-024-00887-2
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DOI: https://doi.org/10.1007/s00182-024-00887-2