Information acquisition interactions in two-player quadratic games


This paper considers two-player quadratic games to examine the relation between strategic interactions in actions and in information decisions. We analyze the role of external effects and of the relative intensities with which the players’ actions interact with the uncertain payoff-relevant parameter. We show that, under some conditions on the quadratic preferences, information choices become substitutes when actions are sufficiently complementary. When attention is restricted to beauty contest games, our results contrast qualitatively with the case studied by Hellwig and Veldkamp (Review of Economic Studies, 76(1)223–251, 2009), where the set of players is a continuum.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3


  1. 1.

    Preferences of this nature have been used to study coordination failures in macroeconomic models (Cooper and John 1988), welfare effects of public information disclosure (Morris and Shin 2002; Hellwig 2005; Cornand and Heinemann 2008; Angeletos and Pavan 2004, 2007; Colombo and Femminis 2008), implications of central bank transparency (Morris and Shin 2005), efficiency properties of communication networks (Calvó-Armengol and de Martí 2007, 2009), endogenous information transmission in networks (Calvó-Armengol et al. 2011; Hagenbach and Koessler 2010), and endogenous communication from party leaders to political activists (Dewan and Myatt 2008, 2012).

  2. 2.

    The information choice is formally described in Sect. 2.3.

  3. 3.

    Consider, for instance, a university changing its library resources, a firm conducting a large market survey, or an organization changing its hardware equipment for information processing.

  4. 4.

    Approximate equilibrium is formally defined by Radner (1980).

  5. 5.

    The formal expression of \(V_{i}\) as a function of these components is provided in the derivation of the value of information in the Appendix.

  6. 6.

    This statement can be easily verified from the derivation of the value of information in the Appendix.

  7. 7.

    Adapting their arguments to our two-player game, the intuitions given by HV make use of the change induced on the variance of a player’s optimal action \(\mathrm{Var}[\tilde{a}^{*}_{i}]=(1-\lambda )^{2}\mathrm{Var}[\tilde{\theta }]+\lambda ^{2}\mathrm{Var}[\tilde{a}^{*}_{j}] +2(1-\lambda )\lambda \mathrm{Cov}[\tilde{a}^{*}_{j},\,\tilde{\theta }]\) by an increase in the other player’s information (p. 229). Notice that the effects on the value of player \(i\)’s additional information due to the two key components \(\lambda ^{2}\partial \mathrm{Var}[\tilde{a}^{*}_{j}]/\partial \omega _{j}\) and \(2(1-\lambda )\lambda \partial \mathrm{Cov}[\tilde{a}^{*}_{j},\,\tilde{\theta }]/\partial \omega _{j}\) can also be analyzed from our expression in (8) above for \(\partial V_{i}/\partial \omega _{j}.\) We note, however, that the sign of \(\partial \mathrm{Var}[\tilde{a}^{*}_{i}]/\partial {\omega _{j}},\) which is implicitly used in HVs intuitive arguments, need not coincide with the sign of \(\partial ^{2}V_{i}/\partial \omega _{i}\partial \omega _{j}.\) The sign of \(\partial \mathrm{Var}[\tilde{a}^{*}_{i}]/\partial {\omega _{j}}\) only reflects how better information to the other player changes the variance of our optimal action, which is useful for intuitive purposes.

  8. 8.

    I thank a referee for pointing out an error, which appeared in a previous version of the paper, and for providing the correct arguments in the interpretation of both (a) the change induced (with more than two players) on the variance of the average action, and of (b) the impact of the variance of the average action on the value of a player’s additional information.

  9. 9.

    Potential games are formally defined by Moderer and Shapley (1996) and Bayesian potential games are formally defined by van Heumen et al. (1996). See Ui (2009) for a general existence result of Bayesian potential games for quadratic games with symmetric cross-derivatives of payoffs.


  1. Angeletos G-M, Pavan A (2004) Transparency of information and coordination in economies with investment complementarities. Am Econ Rev 94(2):91–98

    Article  Google Scholar 

  2. Angeletos G-M, Pavan A (2007) Efficient use of information and social value of information. Econometrica 75(4):1103–1142

    Article  Google Scholar 

  3. Calvó-Armengol A, de Martí J (2007) Communication networks: knowledge and decisions. Am Econ Rev Pap Proc 97(2):86–91

    Article  Google Scholar 

  4. Calvó-Armengol A, de Martí J (2009) Information gathering in organizations: equilibrium, welfare, and optimal network structure. J Eur Econ Assoc 7(1):116–161

    Article  Google Scholar 

  5. Calvó-Armengol A, de Martí J, Prat A (2011) Communication and influence (unpublished manuscript)

  6. Chatterjee K, Harrison TP (1988) The value of information in competitive bidding. Eur J Oper Res 36: 322–333

    Google Scholar 

  7. Colombo L, Femminis G (2008) The social value of public information with costly information acquisition. Econ Lett 100:196–199

    Article  Google Scholar 

  8. Cooper R, John A (1988) Coordinating coordination failures in Keynesian models. Q J Econ 103:441–463

    Article  Google Scholar 

  9. Cornand C, Heinemann F (2008) Optimal degree of public information dissemination. Econ J 118(528): 718–742

    Google Scholar 

  10. Debreu G, Herstein IN (1953) Nonnegative square matrices. Econometrica 21:597–607

    Article  Google Scholar 

  11. Dewan T, Myatt DP (2008) The qualities of leadership: direction, communication, and obfuscation. Am Political Sci Rev 102(3):351–368

    Article  Google Scholar 

  12. Dewan T, Myatt DP (2012) On the rhetorical strategies of leaders: speaking clearly, standing back, and stepping down. J Theor Politics 24(4):431–460

    Article  Google Scholar 

  13. Fudenberg D, Levine D (1986) Limit games and limit equilibria. J Econ Theory 38:261–279

    Article  Google Scholar 

  14. Hagenbach J, Koessler F (2010) Strategic communication networks. Rev Econ Stud 77:1072–1099

    Article  Google Scholar 

  15. Hellwig C (2005) Heterogeneous information and the welfare effects of public information disclosures. UCLA Working Paper

  16. Hellwig C, Veldkamp L (2009) Knowing what others know: coordination motives in information acquisition. Rev Econ Stud 76(1):223–251

    Article  Google Scholar 

  17. Moderer D, Shapley LS (1996) Potential games. Games Econ Behav 14:124–143

    Article  Google Scholar 

  18. Morris S, Shin HS (2002) Social value of public information. Am Econ Rev 92(5):1521–1534

    Article  Google Scholar 

  19. Morris S, Shin HS (2005) Central bank transparency and the signal value of prices. Brook Pap Econ Act 2:1–66

    Article  Google Scholar 

  20. Myatt DP, Wallace C (2012) Endogenous information acquisition in coordinationg games. Rev Econ Stud 79:340–374

    Article  Google Scholar 

  21. Radner R (1962) Team decision problems. Ann Math Stat 33:857–881

    Article  Google Scholar 

  22. Radner R (1980) Collusive behavior in oligopolies with long but finite lives. J Econ Theory 22:136–156

    Article  Google Scholar 

  23. Ui T (2009) Bayesian potentials and information structures: team decision problems revisited. Int J Econ Theory 5:271–291

    Article  Google Scholar 

  24. van Heumen R, Peleg B, Tijs S, Borm P (1996) Axiomatic characterizations and solutions for Bayesian games. Theory Decis 40:103–130

    Article  Google Scholar 

Download references


I am grateful to five anonymous referees and a Co-editor for their extensive feedback which helped to substantially improve the paper. For very useful comments, I also thank Kaniska Dam, Maxim Ivanov, Luciana Moscoso-Boedo, María José Roa-García, Joel Sobel, Laura Veldkamp, and seminar participants at Banco de México, the 2011 SED Meeting at HEC Montreal, and the 2011 Conference on Game Theory at Stony Brook. I gratefully acknowledge financial support from CONACYT (SNI Grant 41286). Any remaining errors are my own.

Author information



Corresponding author

Correspondence to Antonio Jiménez-Martínez.

Additional information

An earlier version was circulated under the title “Strategic Interactions in Information Decisions with a Finite Set of Players” .



Proof of Lemma 1

To ease notation, let us denote by \(E_{i}[\cdot ]=E[\cdot |s_{i},\,\omega ]\) player \(i\)’s expectation operator conditioned on observing signal realization \(s_{i}\) and on the information profile \(\omega .\) From the specification of preferences in (1) and from Assumption 1(i), it follows that, for each information profile \(\omega \in [0,\,1]^{2},\) player \(i\)’s optimal action strategy is given by:

$$\begin{aligned} \alpha ^{*}_{i}\left( s_{i},\,\omega \right) =\rho E_{i}[\tilde{\theta }] +\lambda E_{i}\left[ {\alpha }_{j}\left( \tilde{s}_{j},\,\omega \right) \right] . \end{aligned}$$

By iterating recursively, we obtain

$$\begin{aligned} \alpha ^{*}_{i}\left( s_{i},\,\omega \right) =\rho \sum ^{\infty }_{k=1}\lambda ^{k-1} E_{i}E_{j}E_{i}\cdots E_{p(k)}[\tilde{\theta }], \end{aligned}$$

where \(E_{i}E_{j}E_{i}\cdots E_{p(k)}[\tilde{\theta }]\) denotes the \((k+1)\)-order iterated expectations of \(\tilde{\theta },\) and the player index \(p(k)\) equals \(i\) if \(k\) even and equals \(j\) if \(k\) is odd.

In order to obtain a closed expression for optimal action strategies, we need an operator that allows us to keep track of the discounted nested expectations of the players. To do this, we make use of the knowledge index introduced by Calvó-Armengol and de Martí (2007) in their work on communication in networks. Given the assumed information structure, this knowledge index is specified for our two-player game in the form of the two by two matrix

$$\begin{aligned} \Omega := \begin{pmatrix} 0 &{} \omega _{1} \omega _{2} \\ \omega _{1} \omega _{2} &{} 0 \end{pmatrix}. \end{aligned}$$

Substitution of the posterior expectation \(E_{i}[\tilde{\theta }]=E[\tilde{\theta }|s_{i},\,\omega ]\) given in (4) into the expression for player \(i\)’s optimal action strategy in (9) yields

$$\begin{aligned} \alpha ^{*}_{i}\left( s_{i},\,\omega \right)&= \rho \left( \lambda ^{0}\left[ \mu +\omega _{i}\left( s_{i}-\mu \right) \right] + \lambda ^{1}\left[ \mu +\omega _{j}\omega _{i}\left( s_{i}-\mu \right) \right] \right. \\&+\left. \lambda ^{2}\left[ \mu +\omega _{i}\omega _{j}\omega _{i}\left( s_{i}-\mu \right) \right] +\cdots \right) . \end{aligned}$$

Then, considering \(\omega =(\omega _{1},\,\omega _{2})\) as a vector, the expression above for player \(i\)’s optimal action strategy can be rewritten as

$$\begin{aligned} \alpha ^{*}_{i}\left( s_{i},\,\omega \right)&= \frac{\rho \mu }{1-\lambda }+\rho \omega \cdot \sum ^{\infty }_{k=0} \lambda ^{k}\Omega ^{k} \cdot e_{i}\left( s_{i}-\mu \right) \\&= \frac{\rho \mu }{1-\lambda }+\rho \omega \cdot (I-\lambda \Omega )^{-1} \cdot e_{i}\left( s_{i}-\mu \right) , \end{aligned}$$

where \(I\) denotes the two by two identity matrix and \(e_{i}\) is the \(i\)th vector of the canonical basis of \(\mathbb R ^{2}.\) To verify that the infinite sum \(\sum ^{\infty }_{k=0} \lambda ^{k}\Omega ^{k}=(I-\lambda \Omega )^{-1}\) above is well-defined, we apply a linear algebra result shown by Debreu and Herstein (1953). According to this result, the convergence of \(\sum ^{\infty }_{k=0} \lambda ^{k}\Omega ^{k}\) is guaranteed if \(\left| \lambda \right| \) is strictly less than the inverse of the largest eigenvalue of \(\Omega .\) It can be easily verified that the largest eigenvalue of \(\Omega \) is \(z=+\sqrt{\omega _{1}\omega _{2}} \in [0,\,1]\) so that \(1/z \ge 1\) for all \(\omega _{1},\,\omega _{2} \in [0,\,1].\) Since we are assuming that \(\lambda \in (-1,\,1),\) it follows that \(\left| \lambda \right| <1/z,\) as required.

Then, by computing the inverse of the matrix \((I-\lambda \Omega ),\) we obtain

$$\begin{aligned} \alpha ^{*}_{i}\left( s_{i},\,\omega \right) =\frac{\rho \mu }{1-\lambda }+ \frac{\rho \omega _{i}(1+\lambda \omega _{j})}{1-\lambda ^{2}\omega _{i}\omega _{j}}\left( s_{i}-\mu \right) , \end{aligned}$$

as stated.

Finally, it remains to show that the action strategy profile \(\alpha ^{*}=(\alpha ^{*}_{1},\,\alpha ^{*}_{2})\) that we have obtained is unique. To do this, we follow the approach used by Calvó-Armengol and de Martí (2009) to prove uniqueness of Nash equilibrium for a one-stage, beauty contest game with an exogenously given information choice (Theorem 1). This approach makes use of two ingredients: (a) some available results of existence of potential gamesFootnote 9 with team payoffs for quadratic games, and (b) a central result of team theory due to Radner (1962).

We begin by specifying the two-player team payoff function

$$\begin{aligned} \nu \left( a_{i},\,a_{j},\,\theta \right) :=-(1-\lambda )\left[ \left( a_{i}-\theta \right) ^{2}+\left( a_{j}-\theta \right) ^{2}\right] -\lambda \left( a_{i}-a_{j}\right) ^{2}. \end{aligned}$$

Now, consider a given information profile \(\omega \) and a given signal realization \(s_{i}.\) Because our payoff function \(v\) is assumed to be common for both players, the cross-derivatives of \(v\) with respect to the players’ actions are the same for both of them. Then, since \(v\) is quadratic, it follows from Lemma 6 in Ui (2009) that the payoff \(v\) admits \(\nu \) as a potential. This result implies that, for a fixed pair \((s_{i},\,\omega ),\) a player’s decision problem in actions for our game can be solved using the team payoff function \(\nu .\) Given this, we can then rely on a key result due to Radner (1962) to show that optimal actions are unique in the team game with payoffs \(\nu .\) It follows from Theorem 4 in Radner (1962) that optimal actions are unique for the game with payoffs \(\nu \) if the matrix \(Q:=({\partial ^{2} \nu (a,\,\theta )}/{\partial a_{i}\partial a_{j}})\) is negative definite. For the above specification of \(\nu ,\) we obtain

$$\begin{aligned} Q=2\begin{pmatrix} -1 &{} \lambda \\ \lambda &{} -1 \end{pmatrix}, \end{aligned}$$

a matrix whose eigenvalues are \(z_{1,2}=-1 \pm \sqrt{1-(1-\lambda ^{2})}<0\) for each \(\lambda \in (-1,\,1).\) Therefore, the matrix \(Q\) above is definite negative and optimal actions \(\alpha ^{*}_{i}(s_{i},\,\omega )\) are unique for the given information profile \(\omega \) and the given signal realization \(s_{i}.\) \(\square \)

Derivation of a closed expression for the value of information

Using the specification of preferences given in (1) together with the expression of player \(i\)’s ex-ante expected payoff in (6), we obtain

$$\begin{aligned} V_{i}&= E\left[ v\left( \alpha ^{*}_{i}\left( \tilde{s}_{i},\,\omega \right) ,\,\alpha ^{*}_{j}\left( \tilde{s}_{j},\,\omega \right) ,\,\tilde{\theta }\right) \right] -K\left( \omega _{i}\right) \\&= -E\left[ \alpha ^{*}_{i}\left( \tilde{s}_{i},\,\omega \right) ^{2}\right] -\pi E\left[ \alpha ^{*}_{j}\left( \tilde{s}_{j},\,\omega \right) ^{2}\right] -\delta E\left[ \tilde{\theta }^{2}\right] \\&+2\lambda E\left[ \alpha ^{*}_{i}\left( \tilde{s}_{i},\,\omega \right) \alpha ^{*}_{j}\left( \tilde{s}_{j},\,\omega \right) \right] \\&+2\rho E\left[ \alpha ^{*}_{i}\left( \tilde{s}_{i},\,\omega \right) \tilde{\theta }\right] +2 \phi E\left[ \alpha ^{*}_{j}\left( \tilde{s}_{j},\,\omega \right) \tilde{\theta }\right] -K\left( \omega _{i}\right) . \end{aligned}$$

Then, substitution of the players’ optimal action strategies obtained in Lemma 1 (specified in (7)) into the expression above gives

$$\begin{aligned} V_{i}&= \xi -m^{2}_{i}E\left[ \left( \tilde{s}_{i}-\mu \right) ^{2}\right] -\pi m^{2}_{j} E\left[ \left( \tilde{s}_{j}-\mu \right) ^{2}\right] \\&+2\lambda m_{i}m_{j} E\left[ \left( \tilde{s}_{i}-\mu \right) \left( \tilde{s}_{j}-\mu \right) \right] +2\rho m_{i}E\left[ \left( \tilde{s}_{i}-\mu \right) \tilde{\theta }\right] \\&+2 \phi m_{j}E\left[ \left( \tilde{s}_{j}-\mu \right) \tilde{\theta }\right] -K\left( \omega _{i}\right) , \end{aligned}$$

where \(\xi =2(\rho +\phi )\mu c +(2\lambda -1-\pi )c^{2}-\delta (\sigma ^{2}_{\theta }+\mu ^{2})\) is a term which does not depend on the players’ information choices. Now, using the assumed information structure, the expression above can be rewritten as

$$\begin{aligned} V_{i}\!=\!\xi \!-\!m^{2}_{i}\left[ \sigma ^{2}_{\theta }/\omega _{i}\right] \! -\!\pi m^{2}_{j}\left[ \sigma ^{2}_{\theta }/\omega _{j}\right] +2\lambda m_{i}m_{j}\sigma ^{2}_{\theta }+2\rho m_{i}\sigma ^{2}_{\theta }+2\phi m_{j}\sigma ^{2}_{\theta }\!-\!K\left( \omega _{i}\right) . \end{aligned}$$

By developing the expressions of \(m_{i}\) and \(m_{j}\) given in Lemma 1, we obtain, after rearranging terms,

$$\begin{aligned} V_{i}&= \xi +\frac{\sigma ^{2}_{\theta } \rho ^{2}}{(1-\lambda ^{2}\omega _{i}\omega _{j})^{2}} \big [-\omega _{i}\left( 1+\lambda \omega _{j}\right) ^{2}-\pi \omega _{j}\left( 1+\lambda \omega _{i}\right) ^{2} \\&+2\lambda \omega _{i} \omega _{j}\left( 1+\lambda \omega _{j}\right) \left( 1+\lambda \omega _{i}\right) \\&+2\omega _{i}\left( 1\!+\!\lambda \omega _{j}\right) \left( 1\!-\!\lambda ^{2}\omega _{i}\omega _{j}\right) \!+\!2\left( \frac{\phi }{\rho } \right) \omega _{j}\left( 1\!+\!\lambda \omega _{i}\right) \left( 1\!-\!\lambda ^{2}\omega _{i}\omega _{j}\right) \big ]\!-\!K\left( \omega _{i}\right) . \end{aligned}$$

The above expression gives us player \(i\)’s ex-ante expected payoff (when both players choose their optimal action strategies) as a function of the information profile \(\omega .\) This is our expression of interest to analyze player \(i\)’s incentives to acquire information when player \(j\) changes (locally) the amount of information that she acquires. This approach requires that we compute the second order derivative \(\partial ^{2} V_{i}/\partial \omega _{i}\partial \omega _{j}\) from the expression above. Then, it can be verified that

$$\begin{aligned} \frac{\partial ^{2} V_{i}}{\partial \omega _{i}\partial \omega _{j}}&= \lambda \left[ \frac{\sqrt{2} \sigma _{\theta }\rho }{(1-\lambda ^{2}\omega _{i}\omega _{j})^{2}} \right] ^{2} \Bigg [\left[ 1-\pi + \left( \frac{\phi }{\rho } \right) \right] +[2-\pi ]\lambda \omega _{i} \nonumber \\&+ \left[ 1-2\pi + 2\left( \frac{\phi }{\rho } \right) \right] \lambda \omega _{j} + 4[1-\pi ]\lambda ^{2}\omega _{i}\omega _{j}+[1-2\pi ]\lambda ^{3}\omega ^{2}_{i}\omega _{j} \nonumber \\&+\left[ 2-\pi - 2\left( \frac{\phi }{\rho } \right) \right] \lambda ^{3}\omega _{i}\omega ^{2}_{j}+ \left[ 1-\pi -\left( \frac{\phi }{\rho } \right) \right] \lambda ^{4}\omega ^{2}_{i}\omega ^{2}_{j} \Bigg ]. \end{aligned}$$

In addition, for the particular case of beauty contest payoffs (\(\pi =\lambda ^{2},\,\delta =(1-\lambda )^{2}, \rho =(1-\lambda ),\) and \(\phi =-\lambda (1-\lambda )\)), the expression above yields

$$\begin{aligned} \frac{\partial ^{2}V_{i}}{\partial \omega _{i}\partial \omega _{j}}\bigg |_{\mathrm{bc}}&= \lambda \left[ \frac{\sqrt{2}\sigma _{\theta }(1-\lambda )}{(1-\lambda ^{2}\omega _{i}\omega _{j})^{2}} \right] ^{2} \Bigg [\left[ 1-\lambda -\lambda ^{2}\right] +\left[ 2-\lambda ^{2}\right] \lambda \omega _{i} \nonumber \\&+ \left[ 1-2\lambda -2\lambda ^{2}\right] \lambda \omega _{j} +4\left[ 1-\lambda ^{2}\right] \lambda ^{2}\omega _{i}\omega _{j}+\left[ 1-2\lambda ^{2}\right] \lambda ^{3}\omega ^{2}_{i}\omega _{j} \nonumber \\&+\left[ 2+2\lambda -\lambda ^{2}\right] \lambda ^{3}\omega _{i}\omega ^{2}_{j}+ \left[ 1+\lambda -\lambda ^{2}\right] \lambda ^{4}\omega ^{2}_{i}\omega ^{2}_{j} \Bigg ]. \end{aligned}$$

With the expressions for the second order derivatives of the ex-ante expected payoffs given by (10) and (11) at hand, we proceed to the proofs of the propositions.

Proof of Proposition 1

Notice that the expression in (10) for \(\partial ^{2}V_{i}/\partial \omega _{i}\partial \omega _{j}\) can be rewritten as \({\partial ^{2} V_{i}}/{\partial \omega _{i}\partial \omega _{j}}= h(\lambda ,\,\rho ; \,\omega )f_{i}(\lambda ,\,\pi ,\,(\phi /\rho );\,\omega ),\) where

$$\begin{aligned} h(\lambda ,\,\rho ;\,\omega ):= \lambda \left[ \frac{\sqrt{2}\sigma _{\theta } \rho }{(1-\lambda ^{2}\omega _{i}\omega _{j})^{2}}\right] ^{2}, \end{aligned}$$


$$\begin{aligned} f_{i}(\lambda ,\,\pi ,\,(\phi /\rho );\,\omega )&:= [1-\pi + (\phi /\rho )] +[2-\pi ]\lambda \omega _{i}+[1-2\pi +2(\phi /\rho )]\lambda \omega _{j} \nonumber \\&+4[1-\pi ]\lambda ^{2}\omega _{i}\omega _{j}+[1-2\pi ]\lambda ^{3}\omega ^{2}_{i}\omega _{j} \nonumber \\&+[2-\pi - 2(\phi /\rho )]\lambda ^{3}\omega _{i}\omega ^{2}_{j}+[1-\pi -(\phi /\rho )] \lambda ^{4}\omega ^{2}_{i}\omega ^{2}_{j}.\nonumber \\ \end{aligned}$$

Note first that the sign of \(\partial ^{2}V_{i}/\partial \omega _{i}\partial \omega _{j}\) coincides with the sign of \(h\) when actions are complements, \(\lambda \in (0,\,1),\) and the sign of \(\partial ^{2}V_{i}/\partial \omega _{i}\partial \omega _{j}\) is different from the sign of \(h\) when actions are substitutes, \(\lambda \in (-1,\,0).\) In addition, note that the sign of the function \(h\) is completely determined by the sign of \(\lambda .\) Therefore, if \(f_{i}>0,\) then information decisions feature the same type of strategic interactions as actions. Thus, we proceed by analyzing the sign of the function \(f_{i}\) when \(\lambda \) is close to zero.

  1. (i)

    The result follows directly from the specification of the function \(\partial ^{2}V_{i}/\partial \omega _{i}\partial \omega _{j}\) in (10).

  2. (ii)

    Take a given \(\omega \in (0,\,1)^{2}\) and suppose that the condition \(\phi /\rho >-1+\pi \) holds. Then, we have

    $$\begin{aligned} f_{i}(0,\,\pi ,\,(\phi /\rho );\,\omega )=1-\pi +(\phi /\rho )>0. \end{aligned}$$

The result follows because \(f_{i}(\lambda ,\,\pi ,\,(\phi /\rho );\,\omega )\) is a continuous function in \(\lambda \in (-1,\,1),\) and (a) \(h(\lambda ,\,\rho ;\,\omega )>0\) for each \(\lambda \in (0,\,1)\) whereas (b) \(h(\lambda ,\,\rho ;\,\omega )<0\) for each \(\lambda \in (-1,\,0).\) \(\square \)

Proof of Proposition 2

As in the proof of Proposition 1, we use the fact that \({\partial ^{2} V_{i}}/{\partial \omega _{i}\partial \omega _{j}}= h(\lambda ,\,\rho ;\,\omega )f_{i}(\lambda ,\,\pi ,\,(\phi /\rho );\,\omega ),\) where the functions \(h\) and \(f_{i}\) are specified in (12) and (13), respectively. Note that the sign of the function \(h\) is determined by the sign of \(\lambda .\) Therefore, for \(\lambda >0,\) it follows that information decisions are substitutes if \(f_{i}<0.\) Analogously, for \(\lambda <0,\) it follows that information decisions are complements if \(f_{i}<0\) too. Thus, we proceed by studying the sign of the function \(f_{i}\) when \(\lambda \) approaches 1, (i) below, and when \(\lambda \) approaches -1, (ii) below.

  1. (i)

    Take a given \(\omega \in (0,\,1)^{2}.\) We have

    $$\begin{aligned} \lim _{\lambda \rightarrow 1}f_{i}(\lambda ,\,\pi ,\,(\phi /\rho );\,\omega )&= \left[ 1+2\omega _{i}+\omega _{j}+4\omega _{i}\omega _{j}+\omega ^{2}_{i}\omega _{j}+2\omega _{i}\omega ^{2}_{j}+\omega ^{2}_{i}\omega ^{2}_{j}\right] \\&+\left[ 1+2\omega _{j}-2\omega _{i}\omega ^{2}_{j}-\omega ^{2}_{i}\omega ^{2}_{j}\right] (\phi /\rho )\\&-\left[ 1\!+\!\omega _{i}+2\omega _{j}\!+\!4\omega _{i}\omega _{j}\!+\!2\omega ^{2}_{i}\omega _{j}\!+\!\omega _{i}\omega ^{2}_{j}\!+\!\omega ^{2}_{i}\omega ^{2}_{j}\right] \pi . \end{aligned}$$

    From the expression above, it follows that \(\lim _{\lambda \rightarrow 1}f_{i}(\lambda ,\,\pi ,\,(\phi /\rho );\,\omega )<0\) if and only if \({\phi }/{\rho }<-\kappa _{c}(\omega )+\tau _{c}(\omega )\pi ,\) where \(\kappa _{c}(\omega )\) and \(\tau _{c}(\omega )\) are specified as

    $$\begin{aligned} \kappa _{c}(\omega )&:= \frac{1+2\omega _{i}+\omega _{j}+\omega _{i}\omega _{j}[4+\omega _{i}+2\omega _{j}+\omega _{i}\omega _{j}]}{[1-\omega ^{2}_{i}\omega ^{2}_{j}]+2\omega _{j}[1-\omega _{i}]},\\ \tau _{c}(\omega )&:= \frac{1+\omega _{i}+2\omega _{j}+\omega _{i}\omega _{j}[4+2\omega _{i}+\omega _{j}+\omega _{i}\omega _{j}]}{[1-\omega ^{2}_{i}\omega ^{2}_{j}]+2\omega _{j}[1-\omega _{i}]}. \end{aligned}$$

    Inspection of both expressions above indicates that \(\kappa _{c}(\omega ),\,\tau _{c}(\omega )>0\) for each \(\omega \in (0,\,1)^{2}.\) It remains to show that the subset of \(X=[0,\,1] \times [-1,\,1]\) specified by the inequality \({\phi }/{\rho }<-\kappa _{c}(\omega )+\tau _{c}(\omega )\,\pi \) is nonempty. To do this, it suffices to verify that \(\tau _{c}(\omega )-\kappa _{c}(\omega )>-1.\) Note that

    $$\begin{aligned} \tau _{c}(\omega )-\kappa _{c}(\omega )&= \frac{(\omega _{j}-\omega _{i})(1-\omega _{i}\omega _{j})}{(1-\omega _{i}\omega _{j})(1+\omega _{i}\omega _{j})+2\omega _{j}(1-\omega _{i})} >-1\\&\Leftrightarrow \left( 1-\omega _{i}\omega _{j}\right) \left[ 1+\omega _{j}+\omega _{i}\left( \omega _{j}-1\right) \right] +2\omega _{j}\left( 1-\omega _{i}\right) >0, \end{aligned}$$

    an inequality which holds for each \(\omega \in (0,\,1)^{2}.\) Then, the result follows because \(f_{i}(\lambda ,\,\pi ,\,(\phi /\rho );\,\omega )\) is a continuous function in \(\lambda \in (0,\,1)\) and \(h(\lambda ,\,\rho ;\,\omega )>0\) for each \(\lambda \in (0,\,1).\)

  2. (ii)

    Take a given \(\omega \in (0,\,1)^{2}.\) We have

    $$\begin{aligned} \lim _{\lambda \rightarrow -1}f_{i}(\lambda ,\,\pi ,\,(\phi /\rho );\,\omega )&= \left[ 1-2\omega _{i}-\omega _{j}+4\omega _{i}\omega _{j}-\omega ^{2}_{i}\omega _{j} -2\omega _{i}\omega ^{2}_{j}+\omega ^{2}_{i}\omega ^{2}_{j}\right] \\&+\left[ 1-2\omega _{j}+2\omega _{i}\omega ^{2}_{j}-\omega ^{2}_{i}\omega ^{2}_{j}\right] (\phi /\rho )\\&-\left[ 1-\omega _{i}-2\omega _{j}+4\omega _{i}\omega _{j}-2\omega ^{2}_{i}\omega _{j}-\omega _{i}\omega ^{2}_{j}+\omega ^{2}_{i}\omega ^{2}_{j}\right] \pi . \end{aligned}$$

    From the expression above, it follows that \(\lim _{\lambda \rightarrow -1}f_{i}(\lambda ,\,\pi ,\,(\phi /\rho );\,\omega )<0\) if and only if \({\phi }/{\rho }<-\kappa _{s}(\omega )+\tau _{s}(\omega )\pi ,\) where \(\kappa _{s}(\omega )\) and \(\tau _{s}(\omega )\) are specified as

    $$\begin{aligned} \kappa _{s}(\omega )&:= \frac{1-2\omega _{i}-\omega _{j}+\omega _{i}\omega _{j}[4-\omega _{i}-2\omega _{j}+\omega _{i}\omega _{j}]}{[1-\omega _{i}\omega _{j}][1+\omega _{j}(\omega _{i}-2)]},\\ \tau _{s}(\omega )&:= \frac{1-\omega _{i}-2\omega _{j}+\omega _{i}\omega _{j}[4-2\omega _{i}-\omega _{j}+\omega _{i}\omega _{j}]}{[1-\omega _{i}\omega _{j}][1+\omega _{j}(\omega _{i}-2)]}. \end{aligned}$$

    Notice that the sign of both \(\kappa _{s}(\omega )\) and \(\tau _{s}(\omega )\) depends on the value of \(\omega \in (0,\,1)^{2}.\) Nevertheless, observe that

    $$\begin{aligned} \lim _{\omega _{j} \rightarrow 0} \kappa _{s}(\omega )&= 1-2\omega _{i} \in (-1,\,1)\quad \forall \omega _{i} \in (0,\,1), \\ \lim _{\omega _{j} \rightarrow 0} \tau _{s}(\omega )&= 1-\omega _{i} \in (0,\,1)\quad \forall \omega _{i} \in (0,\,1). \end{aligned}$$

    It trivially follows that \(\lim _{\omega _{j} \rightarrow 0}[\tau _{s}(\omega )-\kappa _{s}(\omega )]=\omega _{i} \in (0,\,1).\) Therefore, for values of \(\omega _{j}\) sufficiently close to 0, we have \(\tau _{s}(\omega ),\,\kappa _{s}(\omega )>0,\) and \(\tau _{s}(\omega )-\kappa _{s}(\omega ) \in (0,\,1).\) Then, the subset of \(X\) specified by \({\phi }/{\rho }<-\kappa _{s}(\omega )+\tau _{s}(\omega )\pi \) is nonempty.

On the other hand, notice that

$$\begin{aligned} \lim _{\omega _{j} \rightarrow 1} \kappa _{s}(\omega )&= 0, \\ \lim _{\omega _{j} \rightarrow 1} \tau _{s}(\omega )&= \frac{(1-\omega _{i})^{2}}{(1-\omega _{i})^{2}}=1. \end{aligned}$$

Therefore, for values of \(\omega _{j}\) sufficiently close to 1, it follows that \(\lim _{\lambda \rightarrow -1}f_{i}(\lambda ,\pi ,\,(\phi /\rho );\,\omega )<0\) if and only if \(\phi /\rho <\pi .\)

The result follows because \(f_{i}(\lambda ,\,\pi ,\,(\phi /\rho );\,\omega )\) is a continuous function in both \(\lambda \in (-1,\,0)\) and in \(\omega \in (0,\,1)^{2},\) and \(h(\lambda ,\,\rho ;\,\omega )<0\) for each \(\lambda \in (-1,\,0).\) \(\square \)

Proof of Proposition 3

The expression in (11) for \(\partial ^{2} V_{i}/\partial \omega _{i}\partial \omega _{j}|_{\mathrm{bc}}\) can be rewritten as \({\partial ^{2} V_{i}}/{\partial \omega _{i}\partial \omega _{j}}|_{\mathrm{bc}}= q(\lambda ;\,\omega )g_{i}(\lambda ;\,\omega ),\) where

$$\begin{aligned} q(\lambda ;\,\omega ):=\lambda \left[ \frac{\sqrt{2} \sigma _{\theta }(1-\lambda )}{(1-\lambda ^{2}\omega _{i}\omega _{j})^{2}} \right] ^{2},\end{aligned}$$


$$\begin{aligned} g_{i}(\lambda ;\,\omega )&:= \left( 1-\lambda -\lambda ^{2}\right) +\left( 2-\lambda ^{2}\right) \lambda \omega _{i}+\left( 1-2\lambda -2\lambda ^{2}\right) \lambda \omega _{j}+4\left( 1-\lambda ^{2}\right) \lambda ^{2}\omega _{i}\omega _{j}\\&+ \left( 1-2\lambda ^{2}\right) \lambda ^{3}\omega ^{2}_{i}\omega _{j} +\left( 2+2\lambda -\lambda ^{2}\right) \lambda ^{3}\omega _{i}\omega ^{2}_{j}+\left( 1+\lambda - \lambda ^{2}\right) \lambda ^{4}\omega ^{2}_{i}\omega ^{2}_{j}. \end{aligned}$$

Note that the sign of \(\lambda \) determines the sign of the function \(q.\) We then turn to analyze the relation between \(\lambda \in (-1,\,1)\) and the sign of \(g_{i}.\)

  1. (i)

    The result follows directly from the specification of the function \(\partial ^{2}V_{i}/\partial \omega _{i}\partial \omega _{j}|_{\mathrm{bc}}\) in (11).

  2. (ii)

    Take a given \(\omega \in (0,\,1)^{2}.\) We have \(g_{i}(0;\,\omega )=1>0.\) Then, the result follows because \(g_{i}(\lambda ;\,\omega )\) is a continuous function in \(\lambda \in (-1,\,1),\) and (a) \(q(\lambda ;\,\omega )>0\) for each \(\lambda \in (0,\,1)\) whereas (b) \(q(\lambda ;\,\omega )<0\) for each \(\lambda \in (-1,\,0).\)

  3. (iii)

    Take a given \(\omega \in (0,\,1)^{2}.\) We have

    $$\begin{aligned} \lim _{\lambda \rightarrow 1}g_{i}(\lambda ;\,\omega )=-1+\omega _{i}\left[ 1+\omega _{i}\omega _{j}\left( \omega _{j}-1\right) \right] +3\omega _{j}\left( \omega _{i}\omega _{j}-1\right) , \end{aligned}$$

    an expression which is strictly negative for each \(\omega \in (0,\,1)^{2}.\) Then, the result follows because \(g_{i}(\lambda ;\,\omega )\) is a continuous function in \(\lambda \in (0,\,1)\) and \(q(\lambda ;\,\omega )>0\) for each \(\lambda \in (0,\,1).\) \(\square \)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Jiménez-Martínez, A. Information acquisition interactions in two-player quadratic games. Int J Game Theory 43, 455–485 (2014).

Download citation


  • Incomplete information
  • Information acquisition
  • Strategic complements
  • Strategic substitutes
  • Externalities

JEL Classification

  • C72
  • D82
  • D83