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Stochastic Value Formation

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Abstract

We propose a simple model of value formation in two societies (communities). When forming values, individuals face conformity pressure within their own society and they are intolerant toward the other society. When such a value formation process is noisy, the interaction between conformity, intolerance and noise can give rise to interesting dynamic outcomes including two polarized societies and polarization across the two societies.

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Notes

  1. We assume that the value space is a closed interval on the real line, which is a common assumption made in the economics literature to study preferences, values, beliefs over a single-dimensional issue. See, for example, Michaeli and Spiro [45, 46] and Cheung and Wu [27].

  2. We use quadratic function because it is commonly adopted in the studies of social norms for tractability. See, for example, Manski and Mayshar [44] and Kuran and Sandholm [40]. Nevertheless, it is the convexity of the intolerance function that leads to our main results.

  3. When we check if a certain state is steady or stable, we drop the time subscript and use \(E_i, E_j\).

  4. The derivation is provided in Anderson et al. [2].

  5. The Fokker–Planck equation is not yet widely used in the economic analysis. See Binmore et al. [10], Friedman and Yellin [30] and Anderson et al. [2, 3] for applications of the Fokker–Planck equation in game theory; and Benhabib et al. [7] for an application of the Fokker–Planck equation to the study of income distributions.

  6. An alternative choice is the strong topology induced by the variation norm. See Bomze [18, 19] and Oechssler and Riedel [47, 48]. The strong topology is more restrictive than the weak topology in the sense that it only allows perturbations from a small fraction of individuals changing their values.

  7. We discretize both the set of values [0,1] (with grid size being 0.0001) and time to make the simulation work. By discretizing time, we use a normal distribution with mean 0 to approximate the noise. In addition, to ensure the dynamic stays in [0, 1], we impose boundary conditions to revert the dynamic back in range every time it escapes. Hence, in the plot we show, a very small portion of the graph is situated outside of [0,1]. The program is available upon request.

  8. The results remain the same if we instead run 100,000 periods.

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Correspondence to Jiabin Wu.

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I sincerely thank the editor, the associate editor and two anonymous reviewers for their constructive suggestions. I also thank Jimmy Hicks for his excellent research assistance.

Appendix

Appendix

Proof of Proposition 1

For any \((\mu _a, \mu _b)\) that satisfies \(\mu _a(\{x\})=\mu _b(\{x\})=1\) for some \(x\in [0, 1]\), we have \(\frac{\partial u_i(x_i)}{\partial x_i}|_{x_i=x}=0\), for \(i \in \{a, b\}\). Therefore, it is a steady state. This gives us Proposition 1 (1).

If \((\mu _a, \mu _b)\) satisfies \(\mu _a(\{0\})=1\) and \(\mu _b(\{1\})=1\), we have \(\frac{\partial u_a(x_a)}{\partial x_a}|_{x_a=0}=-\beta <0\) and \(\frac{\partial u_i(x_b)}{\partial x_b}|_{x_b=1}=\beta >0\). Hence, it is a steady state. Similarly, If \((\mu _a, \mu _b)\) satisfies \(\mu _a(\{1\})=1\) and \(\mu _b(\{0\})=1\), we have \(\frac{\partial u_a(x_a)}{\partial x_a}|_{x_a=1}=\beta >0\) and \(\frac{\partial u_i(x_b)}{\partial x_b}|_{x_b=0}=-\beta <0\). Hence, it is a steady state. This gives us Proposition 1 (2).

When \(\alpha =\beta \), we have \(\frac{\partial u_i(x_i)}{\partial x_i}=E_i-E_j\), for \(i ,j \in \{a, b\}\) and \(i \ne j\). Hence, any \((\mu _a, \mu _b)\) that satisfies \(E_a=E_b\) is a steady state. This gives us Proposition 1 (3).

Finally, we prove Proposition 1 (4). Suppose that \((\mu _a, \mu _b)\) such that \(\mu _a(\{0\})+\mu _a(\{1\})=1\), \(\mu _b(\{0\})+\mu _b(\{1\})=1\), and \(\mu _a(\{1\}), \mu _b(\{1\}) \in (0, 1)\) is a steady state. Then, it must satisfy that, for \(i \in \{a, b\}\)

$$\begin{aligned}&\frac{\partial u_i(x_i)}{\partial x_i}|_{x_i=0}=\alpha \mu _i(\{1\})-\beta \mu _j(\{1\})\le 0, \end{aligned}$$
(6)
$$\begin{aligned}&\frac{\partial u_i(x_i)}{\partial x_i}|_{x_i=1}=(\beta -\alpha )+\alpha \mu _i(\{1\})-\beta \mu _j(\{1\})\ge 0, \ \text {where} \ j \in \{a, b\}, \ j \ne i. \end{aligned}$$
(7)

These give us

$$\begin{aligned}&\beta -\alpha \ge \beta \mu _b(\{1\}) -\alpha \mu _a(\{1\}) \ge 0, \end{aligned}$$
(8)
$$\begin{aligned}&\beta -\alpha \ge \beta \mu _a(\{1\}) -\alpha \mu _b(\{1\}) \ge 0. \end{aligned}$$
(9)

When \(\beta >\alpha \), both can hold at the same time. Q.E.D. \(\square \)

Proof of Proposition 2

Proving the following four statements would give us Proposition 2:

  1. (1)

    \((\mu _a, \mu _b)\) that satisfies either \(\mu _a(\{0\})=\mu _b(\{0\})=1\), or \(\mu _a(\{1\})=\mu _b(\{1\})=1\), is not stable.

  2. (2)

    There is no stable state in which there exists \(x \in (0, 1)\), such that for one of the society \(i\in \{a, b\}\), \(\frac{\partial u_i(x_i)}{\partial x_i}|_{x_i=x}=0\) and x is in the support of \(\mu _i\).

  3. (3)

    \((\mu _a, \mu _b)\) that satisfies for \(i \in \{a, b\}\), such that \(\mu _i(\{0\})+\mu _i(\{1\})=1\) and \(\mu _i(\{0\}), \mu _i(\{1\})>0\), is not stable.

  4. (4)

    \((\mu _a, \mu _b)\) that satisfies either \(\mu _a(\{0\})=1\) and \(\mu _b(\{1\})=1\), or \(\mu _a(\{1\})=1\) and \(\mu _b(\{0\})=1\), is stable.

For \((\mu _a, \mu _b)\) that satisfies \(\mu _a(\{0\})=\mu _b(\{0\})=1\), consider an alternative state \((\nu _a, \nu _b)\) such that \(\nu _a(\{\varepsilon \})=1\) and \(\nu _b(\{0\})=1\), for some \(\varepsilon \in (0, 1)\). Then, under \((\nu _a, \nu _b)\)

$$\begin{aligned}&\frac{\partial u_a(x_a)}{\partial x_a}|_{x_i=\varepsilon }=\beta \varepsilon > 0, \ \frac{\partial u_b(x_b)}{\partial x_b}|_{x_b=0}=-\beta \varepsilon <0, \end{aligned}$$
(10)

implying that individuals in society a moves toward higher values, and individuals in society b remain holding value 0. Since this is true for any \(\varepsilon \in (0, 1)\), the dynamic keeps evolving until everyone in society a holds value 1 while society b stays at 0. Hence, \((\mu _a, \mu _b)\) that satisfies \(\mu _a(\{0\})=\mu _b(\{0\})=1\) is not stable. Similarly, \((\mu _a, \mu _b)\) that satisfies \(\mu _a(\{1\})=\mu _b(\{1\})=1\) is not stable either. This gives us statement (1).

Suppose \((\mu _a, \mu _b)\) is a steady state such that there exists \(x \in (0, 1)\), such that for one of the society \(i\in \{a, b\}\), \(\frac{\partial u_i(x_i)}{\partial x_i}|_{x_i=x}=0\) and x is in the support of \(\mu _i\). First, assume that \(\alpha =\beta \). In this case, we must have \(E_a=E_b=\bar{x}\), for some \(\bar{x} \in (0, 1)\). Consider a shift from \((\mu _a, \mu _b)\) to \((\nu _a, \nu _b)\) such that \(E_a=\bar{x}-\varepsilon \) for some arbitrarily small positive \(\varepsilon \) and \(\nu _b=\mu _b\). Then, we have

$$\begin{aligned}&\frac{\partial u_a(x_a)}{\partial x_a}=-\varepsilon < 0, \ \frac{\partial u_b(x_b)}{\partial x_b}=\varepsilon >0, \end{aligned}$$
(11)

implying that the entire distribution in society a shifts to the left, and the entire distribution in society b shifts to the right, which further ensures that \(E_a<E_b\). Hence, the dynamic keeps evolving until everyone in society a holds value 0 and everyone in society b holds value 1. Therefore, when \(\alpha =\beta \), there is no stable state in which there exists \(x \in (0, 1)\), such that for one of the society \(i\in \{a, b\}\), \(\frac{\partial u_i(x_i)}{\partial x_i}|_{x_i=x}=0\) and x is in the support of \(\mu _i\).

Next, assume that \(\alpha \ne \beta \). Without loss of generality, assume that there exists a steady state \((\mu _a, \mu _b)\) such that a \(x\in (0, 1)\) is in the support of \(\mu _a\). It must satisfy:

$$\begin{aligned} \frac{\partial u_a(x_a)}{\partial x_a}|_{x_a=x}=(\beta -\alpha )x+\alpha E_a-\beta E_b=0. \end{aligned}$$
(12)

We obtain that \(x=\frac{\beta E_b-\alpha E_a}{\beta -\alpha }\), which is unique. Hence, there exists no other value in (0, 1) in the support of \(\mu _a\). This implies that \(\mu _a\) must satisfy \(\mu _a(\{x\})>0\) and \(\mu _a(\{0\})+\mu _a(\{x\})+\mu _a(\{1\})=1\).

Consider a shift from \((\mu _a, \mu _b)\) to \((\nu _a, \nu _b)\) such that \(\nu _a(\{0\})=\mu _a(\{0\})\), \(\nu _a(\{1\})=\mu _a(\{1\})\), \(\nu _a(\{x-\varepsilon \})=\mu _a(\{x\})\) for some arbitrarily small positive \(\varepsilon \) and \(\nu _b=\mu _b\). Then, we have

$$\begin{aligned}&\frac{\partial u_a(x_a)}{\partial x_a}|_{x_a=x-\varepsilon }=(\alpha (1-\mu _a(\{x\}))-\beta )\varepsilon . \end{aligned}$$
(13)

When \(\alpha >\beta \), we have

$$\begin{aligned}&\frac{\partial u_a(x_a)}{\partial x_a}|_{x_a=0}=\alpha E_a-\beta E_b>\frac{\partial u_a(x_a)}{\partial x_a}|_{x_a=x}=0, \end{aligned}$$
(14)
$$\begin{aligned}&\frac{\partial u_a(x_a)}{\partial x_a}|_{x_a=1}=(\beta -\alpha )+\alpha E_a-\beta E_b<\frac{\partial u_a(x_a)}{\partial x_a}|_{x_a=x}=0. \end{aligned}$$
(15)

Therefore, we must have \(\mu _a(\{0\})=\mu _a(\{1\})=0\) and \(\mu _a(\{x\})=1\). Hence, \(\frac{\partial u_a(x_a)}{\partial x_a}|_{x_a=x-\varepsilon }<0\). When \(\alpha <\beta \), \(\frac{\partial u_a(x_a)}{\partial x_a}|_{x_a=x-\varepsilon }=(\alpha (1-\mu _a(\{x\}))-\beta )\varepsilon<(\alpha -\beta )\varepsilon <0\). Therefore, the mass on value \(x-\varepsilon \) in society a is moving to the left.

If \(\mu _a(\{0\})>0\), we must have \(\frac{\partial u_a(x_a)}{\partial x_a}|_{x_a=0}=\alpha E_a-\beta E_b=\alpha (\mu _a(\{x\})x+\mu _a(\{1\}))-\beta E_b\le 0\) under \((\mu _a, \mu _b)\). Then, under \((\nu _a, \nu _b)\), \(\frac{\partial u_a(x_a)}{\partial x_a}|_{x_a=0}=\alpha (\mu _a(\{x\})(x-\varepsilon )+\mu _a(\{1\}))-\beta E_b<0\), implying that the mass on value 0 in society a if there is any remains the same. If \(\mu _a(\{1\})>0\), we must have \(\frac{\partial u_a(x_a)}{\partial x_a}|_{x_a=1}=(\beta -\alpha )+\alpha E_a-\beta E_b=(\beta -\alpha )+\alpha (\mu _a(\{x\})x+\mu _a(\{1\}))-\beta E_b\ge 0\) under \((\mu _a, \mu _b)\). Then, under \((\nu _a, \nu _b)\), \(\frac{\partial u_a(x_a)}{\partial x_a}|_{x_a=0}=(\beta -\alpha )+\alpha (\mu _a(\{x\})(x-\varepsilon )+\mu _a(\{1\}))-\beta E_b\), which can be negative or positive. Hence, the mass on value 1 in society a if there is any either remains the same or is moving to the left. Hence, \(E_a\) is decreasing.

In society b, if there exists \(y \in (0, 1)\), such that \(\frac{\partial u_b(x_b)}{\partial x_b}|_{x_b=y}=0\) and y is in the support of \(\mu _b\), then because \(E_a\) is decreasing, the mass on value y in society b is moving to the right, the mass on value 0 in society b if there is any is moving to the right or remains the same, and the mass on value 1 on society b if there is any remains the same. Hence, the dynamic keeps evolving until the two societies reach a state with polarization across the two societies (\(\mu _a(\{0\})=1, \mu _b(\{1\})=1\)), or with at least one of the society is polarized ( there exists a \(i \in \{a, b\}\), such that \(\mu _i(\{0\})+\mu _i(\{1\})=1\) and \(\mu _i(\{0\}), \mu _i(\{1\})>0\)). Therefore, when \(\alpha \ne \beta \), there is no stable state in which there exists \(x \in (0, 1)\), such that for one of the society \(i\in \{a, b\}\), \(\frac{\partial u_i(x_i)}{\partial x_i}|_{x_i=x}=0\) and x is in the support of \(\mu _i\). This concludes the proof of statement (2).

Consider \((\mu _a, \mu _b)\) that satisfies for \(i \in \{a, b\}\), such that \(\mu _i(\{0\})+\mu _i(\{1\})=1\) and \(\mu _i(\{0\}), \mu _i(\{1\})>0\). Suppose \((\mu _a, \mu _b)\) shifts to \((\nu _a, \nu _b)\) such that \(\nu _a(\{0\})=\mu _a(\{0\})+\varepsilon \) and \(\nu _a(\{1\})=\mu _a(\{1\})-\varepsilon \), for some arbitrarily small positive \(\varepsilon \) and \(\nu _b=\mu _b\). Under \((\nu _a, \nu _b)\), \(\frac{\partial u_a(x_a)}{\partial x_a}|_{x_a=0}=\alpha (\mu _a(\{1\})-\varepsilon )-\beta \mu _b(\{1\})<\alpha \mu _a(\{1\})-\beta \mu _b(\{1\}) \le 0\). Hence, the mass on 0 in society a remains the same. \(\frac{\partial u_a(x_a)}{\partial x_a}|_{x_a=1}=(\beta -\alpha )+\alpha (\mu _a(\{1\})-\varepsilon )-\beta \mu _b(\{1\})\), which can be either positive or negative. Hence, the mass on value 1 in society a either remains the same or is moving to the left. Since \(E_a\) is smaller under \((\nu _a, \nu _b)\) than that under \((\mu _a, \mu _b)\), the mass on value 0 in society b remains the same or is moving to the right, the mass on value 1 in society b remains the same, and \(E_b\) under \((\nu _a, \nu _b)\) is either equal to or bigger than that under \((\mu _a, \mu _b)\). Therefore, either \((\nu _a, \nu _b)\) is a new steady state, or it sets the motion for the dynamic to move away from \((\mu _a, \mu _b)\). This gives us statement (3).

Finally, consider \((\mu _a, \mu _b)\) that satisfies \(\mu _a(\{0\})=1\) and \(\mu _b(\{1\})=1\). For any \((\nu _a, \nu _b) \in U^\varepsilon \), given the definition of the Prohorov metric, we must have \(\kappa (\mu _a, \nu _a)<\varepsilon \) and \(\kappa (\mu _b, \nu _b)<\varepsilon \). Hence, under \((\nu _a, \nu _b)\), for any \(x \in [0, 1]\), and for sufficiently small positive \(\varepsilon \),

$$\begin{aligned}&\frac{\partial u_a(x_a)}{\partial x_a}|_{x_a=x}= (\beta -\alpha )x+\alpha E_a-\beta E_b \nonumber \\&\quad \le {\left\{ \begin{array}{ll} \beta -\alpha +\alpha \varepsilon -\beta (1-\varepsilon )=(\alpha +\beta )\varepsilon -\alpha<0 &{} \text {if } \beta -\alpha \ge 0, \\ \alpha \varepsilon -\beta (1-\varepsilon )=(\alpha +\beta )\varepsilon -\beta<0 &{} \text {if } \beta -\alpha <0. \end{array}\right. } \end{aligned}$$
(16)
$$\begin{aligned}&\frac{\partial u_b(x_b)}{\partial x_b}|_{x_a=x}= (\beta -\alpha )x+\alpha E_b-\beta E_a \nonumber \\&\quad \ge {\left\{ \begin{array}{ll} \alpha (1-\varepsilon )-\beta \varepsilon =\alpha -(\alpha +\beta )\varepsilon>0 &{} \text {if } \beta -\alpha \ge 0, \\ (\beta -\alpha )+\alpha (1-\varepsilon )-\beta \varepsilon =\beta -(\alpha +\beta )\varepsilon >0 &{} \text {if } \beta -\alpha <0. \end{array}\right. } \end{aligned}$$
(17)

Hence, the distribution in society a moves back to \(\mu _a\) and the distribution in society b moves back to \(\mu _b\), implying that \((\mu _a, \mu _b)\) that satisfies \(\mu _a(\{0\})=1\) and \(\mu _b(\{1\})=1\), is stable. Similarly, \((\mu _a, \mu _b)\) that satisfies \(\mu _a(\{1\})=1\) and \(\mu _b(\{0\})=1\), is stable as well. This concludes the proof of statement (4). Q.E.D. \(\square \)

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Wu, J. Stochastic Value Formation. Dyn Games Appl 11, 597–611 (2021). https://doi.org/10.1007/s13235-020-00370-z

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