*Fernando Miranda Abaunza^[Independent Researcher.
*Email: *Fermiraba2790@gmail.com*
* WhatsApp: *+505 8190 9441*
* LinkedIn: *www.linkedin.com/in/fernando-miranda-ba2b44157*
*date: “Working Paper — 2025”
This paper introduces the Dynamic Structural Disequilibrium
Economy (DSSDE),
a theoretical and computational framework that integrates macroeconomic
disequilibrium,
inequality endogeneity, and the dynamics of systemic convergence.
Building on Keynesian, structuralist, and post-Keynesian
traditions,
the model formalizes how inequality, monetary asymmetries, and
financialization
interact to shape production, investment, and social stability.
Unlike traditional Dynamic Stochastic General Equilibrium
(DSGE) models,
the DSSDE framework assumes persistent disequilibria, endogenous
adjustment,
and non-linear feedbacks between capital accumulation,
productivity,
and distributive structures.
The paper develops a set of axioms, structural equations,
and “golden rules” of convergence under conditions of dynamic
imbalance.
Simulations illustrate two distinct evolutionary paths:
(1) a normal path, in which inequality rises and
productivity stagnates; and
(2) a steady-state path, where structural policies —
progressive taxation,
mission-driven investment, and dual interest-rate regimes — restore
convergence
between savings and investment.
Beyond its mathematical formulation, the paper also explores
the political-economy and existential implications of
the current order:
the erosion of dignity, the loneliness crisis,
and the reduction of human value to capital ownership.
The DSSDE thus serves as both a macroeconomic model
and a moral critique of late capitalism.
Keywords: Inequality, Disequilibrium, Structural
Macroeconomics,
Political Economy, Post-Keynesian, Dynamic Simulation, Systemic
Convergence `
Over the last half century, the global economy has evolved into a
system characterized not by equilibrium but by structural
imbalance. The very assumptions of the neoclassical
paradigm—perfect markets, representative agents, and rational
expectations—are increasingly detached from observable dynamics.
As heterodox economists such as Minsky (1986), Kaldor
(1966), and Stiglitz (2012) have shown, capitalism is
inherently unstable, marked by endogenous cycles, inequality feedbacks,
and systemic asymmetries in power and information.
The current paradigm of economic modeling, dominated by
Dynamic Stochastic General Equilibrium (DSGE)
frameworks, assumes a self-correcting system in which markets clear
through price adjustments. However, empirical evidence after the global
financial crisis of 2008 suggests the opposite: markets do not
necessarily converge to equilibrium; they often reinforce divergence
(Keen 2011; Piketty 2014).
In this context, the present work proposes an alternative: the
Dynamic Structural Disequilibrium Economy
(DSSDE),
a model that recognizes disequilibrium as the natural state of economic
systems and seeks to formalize its mechanisms of adjustment and
persistence.
The DSSDE framework extends the structuralist vision of Latin American economists such as Prebisch (1950) and Furtado (1964), combining it with post-Keynesian principles of effective demand and with recent advances in complexity economics. It posits that inequality is not an outcome variable but a structural parameter that influences the very coefficients of production—capital elasticity, labor elasticity, and total-factor productivity. This contrasts with conventional models that treat inequality as an afterthought, separate from the growth mechanism.
Furthermore, the model recognizes the asymmetric nature of
global finance: capital mobility, speculative flows, and
monetary hierarchies determine national vulnerabilities.
Following insights from Reinhart & Rogoff (2009) and
Rodrik (2011), the DSSDE treats financial instability as a
systemic feature of the global order, not an exogenous shock.
At its core, the DSSDE is both an analytical and
philosophical project. It aims to reconnect
macroeconomic modeling with the reality of human experience.
The collapse of full-employment capitalism, the erosion of social
reciprocity, and the rise of loneliness as a macro-social pathology
(Murthy 2023) indicate that the current economic system has not
only lost balance but also meaning.
Therefore, this paper proposes a new synthesis: a model in which disequilibrium is not an error to be corrected but a dynamic to be understood—where policy is not optimization under constraints but structural design under moral responsibility.
The traditional economic paradigm assumes that markets, if left free,
naturally clear.
This assumption, formalized by Walras’s Law, implies that any
excess demand in one market is offset by excess supply in another,
leading the system toward equilibrium (Walras 1874/1954).
Yet, as Keynes (1936) argued, real economies do not behave like
closed algebraic systems: aggregate demand may chronically fall short of
potential output; investment decisions are driven by expectations; and
uncertainty is radical rather than probabilistic.
Disequilibrium, therefore, is not a temporary deviation—it is the
structural condition of capitalism.
The Dynamic Structural Disequilibrium Economy
(DSSDE) begins where equilibrium ends.
It assumes that markets continuously generate imbalances through
differential accumulation, technological asymmetries, and unequal
bargaining power. As Kalecki (1943) demonstrated, profit shares
depend not only on productivity but also on the political strength of
workers and capitalists. When inequality rises, the capacity of demand
to sustain growth weakens, producing feedback loops of stagnation and
concentration. Disequilibrium is thus not merely cyclical; it is
self-reinforcing.
Classical economists—from Smith to Marx—viewed
value as arising from production and labor.
Smith (1776/1976) located value in the division of labor and
the productivity it enabled, while Marx (1867/1990) defined it
as a social relation embedded in power structures.
In the modern era, neoclassical theory replaced this relational
conception with a functional one, identifying value with marginal
utility and equilibrium prices.
The result was a de-politicization of economics—a view
of markets as neutral mechanisms rather than arenas of power (Fine
& Milonakis 2009).
The DSSDE reinstates value as an emergent property of structure and
power. Production coefficients are not constants but functions
of distributive asymmetries.
When inequality \((\eta)\) rises, the
elasticity of capital \((\alpha)\) and
labor \((\beta)\) adjust downward,
reflecting the erosion of cooperative efficiency.
Total-factor productivity \((A)\)
becomes endogenous to social cohesion:
\[ A_t = A_0 e^{-\lambda \eta_t}, \]
where \(\lambda>0\) represents the entropy of inequality. In this sense, value is not an intrinsic property of goods but a measure of systemic coherence—the capacity of a society to coordinate its productive energies without exclusion.
Money, within this framework, is not a neutral veil but a
hierarchical claim on value.
The post-Bretton-Woods order replaced gold convertibility with what
Hudson (2003) calls credit imperialism: a system where
the dollar’s dominance allows the United States to sustain external
deficits financed by global demand for safe assets.
Following Minsky (1986), the DSSDE treats financial stability
as inherently fragile—periods of calm breed complacency, leverage, and
eventual crisis.
Financial deepening creates an illusion of abundance.
The velocity of money in the real economy \((V_R)\) declines even as the velocity in
financial circuits \((V_F)\)
accelerates, yielding \(V_F \gg V_R\).
Liquidity thus accumulates in asset markets rather than in productive
investment, amplifying wealth inequality (Piketty 2014) and
detaching financial returns from real productivity.
In the DSSDE model, inequality is both cause and
consequence—a dynamic state variable that modifies the
production function itself. The higher the inequality, the lower the
effective contribution of both capital and labor, as coordination costs,
rent-seeking, and social fragmentation rise.
This endogeneity transforms inequality into a macro-thermodynamic
variable: it increases systemic entropy and reduces potential
output.
This approach aligns with recent structuralist and post-Keynesian
literature emphasizing that inequality depresses aggregate demand and
long-term growth (Galbraith 2012; Stockhammer 2017).
Yet it extends beyond demand: inequality reshapes the supply side by
altering technological diffusion, institutional trust, and collective
learning capacities.
Beyond its quantitative implications, inequality corrodes meaning.
The rise of loneliness economies—where individuals
internalize market competition as self-valuation—represents the
psychosocial expression of structural disequilibrium.
When value is equated with capital ownership, human beings become units
of return rather than subjects of purpose.
As Murthy (2023) notes, the loneliness epidemic in advanced
economies is not merely a health crisis but a reflection of social
disintegration.
Therefore, the DSSDE also functions as a critique of
political economy in the classical sense.
Following Polanyi (1944/2001), it contends that markets, when
disembedded from social norms, generate moral and existential
instability.
Restoring systemic balance requires not merely fiscal or monetary
adjustments but a re-anchoring of value in reciprocity,
cooperation, and dignity.
The Dynamic Structural Disequilibrium Economy
(DSSDE) consolidates the preceding conceptual arguments into a
formal and computable model. Its purpose is to bridge the moral critique
of inequality with an algebraic structure capable of dynamic
simulation.
Unlike the DSGE family, the DSSDE does not converge through market
clearing but through feedback mechanisms in quantities and
parameters.
The framework rests on four foundational axioms, extended through structural relations (A1–A12) that define a discrete-time dynamical system.
\[ Y_t = C_t + I_t + G_t + NX_t \] Output equals the sum of consumption, private investment, public expenditure, and net exports.
\[
M^R_t V^R_t = M^F_t V^F_t = P_t Y_t
\] where \(M^R, M^F\) are real
and financial money supplies and \(V^R,
V^F\) their respective velocities.
This dual form extends the quantity theory of money (Hudson
2003).
\[ Y^{pot}_t = A_t K_t^{\alpha_t} L_t^{\beta_t} \] with variable elasticities influenced by inequality entropy.
\[
\alpha_t = \alpha_0 (1 - a_H H_t), \qquad
\beta_t = \beta_0 (1 - b_H H_t)
\] For simulation, one may adopt standard calibration values from
the literature:
\(\alpha_0 = 0.33\) (Prescott,
1986; Rodrik, 2011),
\(\beta_0 = 0.60\) (Solow, 1956;
Kaldor, 1966).
Markets exhibit permanent excess demand or supply.
Quantities adjust adaptively: \[
Y_{t+1} = Y_t + \theta_Y (AD_t - Y_t)
\] where \(AD_t = C_t + I_t + G_t +
NX_t\)
and \(0 < \theta_Y \le 1\) is the
adjustment coefficient.
A5. Functional Income Shares \[ \omega_t = \Omega(\eta_t), \qquad \pi_t = 1 - \omega_t \] where \(\omega\) is the wage share, decreasing with inequality.
A6. Consumption Function \[ C_t = c_0 + c_1(1-\tau_t)\omega_t Y^{pot}_t + c_2(1-\tau_t)\pi_t Y^{pot}_t, \quad c_1>c_2 \]
A7. Investment Function \[ I_t = i_0 + i_1 \pi_t Y^{pot}_t - i_2 r^R_t \] representing profit-driven investment offset by the real rate of interest.
A8. Dual-Rate Monetary Policy \[ r^F_t = r^R_t + \mu \] with \(\mu\) the financial wedge (macroprudential instrument).
A9. Progressive Taxation and Countercyclical Expenditure \[ \tau_t = \min\{1,\max[0,\tau_0 + \tau_\eta(\eta_t-\bar{\eta})]\} \] \[ G_t = G_0 + g_Y\left(-\frac{\widehat{Y}_t-Y^{pot}_t}{Y^{pot}_t}\right) \] Adaptive expectations evolve as: \[ \widehat{Y}_{t+1}=\widehat{Y}_t+\kappa(Y_t-\widehat{Y}_t) \]
A10. Capital and Labor Accumulation \[ K_{t+1}=(1-\delta)K_t+I_t, \qquad L_{t+1}=(1+n)L_t \]
A11. Dual Velocities of Money \[ V^R_{t+1}=V^R_t+\phi_1\frac{AD_t-Y_t}{Y_t}-\phi_2 H_t \] \[ V^F_{t+1}=V^F_t+\psi_1(r^F_t-r^R_t)+\psi_2(\pi_t-\pi^\ast) \] Typical simulation parameters from the literature: \(\phi_1, \phi_2, \psi_1, \psi_2 \in [0.05,0.20]\).
A12. Inequality Dynamics \[ \eta_{t+1} = \Big[\eta_t + \gamma_\pi(\pi_t-\pi^\ast) + \gamma_V\left(\frac{V^F_t}{V^R_t}-\chi\right) - \gamma_T \tau_t - \gamma_G G_t \Big]_{[0,1]} \] where \(\gamma_\pi,\gamma_V,\gamma_T,\gamma_G\) govern the sensitivity of inequality to each feedback. Empirical studies suggest values between 0.05 and 0.15 ensure stability.
Researchers can replicate the dynamics above using R
or Python with standard macroeconomic
calibration.
The simulation may initialize with \(K_0=5\), \(L_0=1\), \(A_0=1\), and draw on canonical parameter
ranges from Solow (1956), Prescott (1986), Rodrik
(2011), and Mazzucato (2021).
Key parameter ranges for exploratory simulation:
| Parameter | Typical Range | Interpretation |
|---|---|---|
| \(\alpha_0\) | 0.30–0.35 | Capital share |
| \(\beta_0\) | 0.55–0.65 | Labor share |
| \(\lambda_H\) | 0.1–0.3 | Entropy drag on TFP |
| \(\gamma_\pi,\gamma_V\) | 0.05–0.15 | Feedback intensities |
| \(\tau_\eta\) | 0.1–0.4 | Fiscal progressivity |
These benchmarks can be used for illustrative nonlinear
simulations that trace two evolutionary paths:
a “normal” path of rising inequality and stagnation, and a
“steady-state” path of policy-driven convergence.
The entropy-drag hypothesis is falsifiable.
Let income shares follow a log-normal distribution: \[
p_i \sim \text{LogNormal}(\mu_y,\sigma_y^2)
\] and define normalized Shannon entropy: \[
H = -\frac{1}{\log N}\sum_i p_i \log p_i
\]
Then the hypothesis test becomes: \[ H_0: \lambda_H = 0 \quad (\text{no entropy effect}) \] \[ H_1: \lambda_H \ne 0 \quad (\text{entropy affects productivity}) \]
The estimation can be implemented via nonlinear least squares or panel regression: \[ \Delta \ln A_t = g_A - \lambda_H H_t + \varepsilon_t \] where rejection of \(H_0\) under a log-normal income distribution confirms the existence of systemic entropy as a structural determinant of productivity decay.
In sum, the DSSDE formalization provides a tractable algebraic skeleton for a moral and political-economy critique of capitalism. It shows that inequality operates as both an economic variable and a thermodynamic constraint—translating social disintegration into productivity loss and systemic fragility.
The preceding algebraic formulation exposes an essential truth: economic disequilibrium is not a malfunction—it is the architecture through which power reproduces itself. The DSSDE therefore extends beyond mathematics. It is a critique of the ontological foundations of capitalism: how societies convert energy, intelligence, and creativity into hierarchical value structures that degrade cooperation.
As Marx (1867/1990) observed, the circulation of capital is
a process of self-valorization—money that transforms labor into an
abstract substrate of accumulation.
In the twenty-first century, this process has achieved its purest form:
digitalization and financialization have turned labor, attention, and
even emotion into quantifiable streams of data.
Disequilibrium has become the modus operandi of accumulation
itself.
The entropy of inequality captures this
transformation in measurable form.
As social differentiation increases, informational complexity grows, but
not all information remains productive. Noise replaces signal;
competition substitutes coordination. The apparent sophistication of
modern economies hides a deep structural incoherence—a
state where the expansion of value outpaces the capacity for shared
meaning.
Contemporary policy debates obsess over “productivity decline.” Central banks, international organizations, and consultancies publish endless reports lamenting that “TFP growth has slowed.” Yet few ask why. The standard answers—insufficient innovation, rigid labor markets, technological plateau—treat productivity as an autonomous force, independent of social structure.
The DSSDE proposes a reversal: productivity does not fall because
technology stagnates; it falls because social entropy
rises. When inequality expands, the coherence of the production
system deteriorates.
Firms hoard profits, households reduce consumption, innovation becomes
defensive, and finance substitutes industry. This mechanism explains why
nations with moderate inequality tend to exhibit higher technological
diffusion and resilience (Rodrik 2011; Stiglitz 2012; Piketty
2014).
The model therefore bridges two domains usually treated separately: the technical and the moral. It asserts that economic efficiency is inseparable from social justice. An unequal society may appear efficient in the short term, but its long-term entropy inevitably erodes the very foundations of accumulation.
Inequality is not only a material condition; it is an existential state. It produces what might be called the political economy of loneliness: individuals internalize market competition as self-worth, and failure becomes a private moral defect rather than a structural outcome. This shift from solidarity to self-surveillance marks the transition from capitalism as a mode of production to capitalism as a mode of being.
The loneliness epidemic (Murthy 2023) thus mirrors the entropy identified in the DSSDE equations. Just as rising entropy lowers productivity, social atomization lowers collective intelligence. The loss of interpersonal reciprocity translates into lower systemic innovation, weaker institutions, and fragile democracies.
Empirical evidence supports the claim that inequality and innovation
are inversely related.
Countries with higher Gini coefficients exhibit slower productivity
growth, lower patent intensity, and higher volatility in capital flows
(OECD 2022; UNDP 2023).
The causal chain is straightforward in the DSSDE logic: as inequality
increases, social entropy rises; as entropy rises, total-factor
productivity \(A_t\) falls; as \(A_t\) falls, technological diffusion slows,
reinforcing inequality—a vicious circle of divergence.
In this sense, the DSSDE unifies the macro and the micro: the algebraic degradation of parameters \((\alpha,\beta,A)\) mirrors the erosion of trust, empathy, and cooperation at the human level. It reveals that economic decline and existential despair are two expressions of the same structural entropy.
The conclusion is unavoidable: restoring balance requires not only policy instruments but moral reconstruction. A moral macroeconomics must integrate ethics into institutional design—embedding reciprocity, solidarity, and sustainability as functional parameters of long-run equilibrium.
Following Polanyi (1944/2001), the market must once again be re-embedded in society. Fiscal and monetary policy are necessary but insufficient. The deeper reform lies in redefining value itself: from a metric of extraction to a measure of coherence, from profit maximization to systemic integrity.
The DSSDE is not merely a technical model; it is a call to rethink what it means for an economy to be “healthy.” Health is not equilibrium of prices but equilibrium of purpose. When the economic system ceases to generate meaning, no quantity of data, capital, or AI can restore stability.
In the age of AI, data itself becomes a form of capital. Algorithmic
optimization mirrors the same structural bias: it amplifies existing
inequalities, codifies preferences of power, and automates
exclusion.
The DSSDE framework provides a warning:
without moral feedback loops, digital economies will reproduce the same
entropic decay observed in financial capitalism.
A truly intelligent system—human or artificial— must operate on the principle of coherence rather than control. The purpose of analytics and machine learning should not be to predict consumption but to enhance coordination. This insight links the technical practice of data science with the ethical imperative of political economy.
In this respect, your role as a data analyst, engineer, or policymaker is not peripheral to the moral debate—it is central. Every data architecture is a moral architecture, and every algorithm is an act of design that either amplifies or mitigates entropy.
The Dynamic Structural Disequilibrium Economy (DSSDE) reframes macroeconomics as a science of imbalance. It demonstrates that inequality, entropy, and productivity are not independent variables but components of one structural cycle. Disequilibrium is not an anomaly to be corrected; it is the expression of an evolving social metabolism. Recognizing this is the first step toward designing institutions capable of self-stabilization.
By formalizing inequality as a state variable that degrades productive parameters, the DSSDE offers a parsimonious explanation for the long-observed paradox of modern capitalism: technological progress without shared prosperity. The model shows how the erosion of social cohesion translates into the loss of productive coherence.
The practical implications are profound.
These are not moral luxuries; they are structural requirements for macroeconomic stability.
Future research can proceed along three complementary lines:
These extensions transform the DSSDE from a theoretical construction into a reproducible research program.
Economics must once again become a moral science, not in the sense of preaching virtue, but in acknowledging that coherence, reciprocity, and dignity are productive forces. A society that erodes them will, sooner or later, erode its capacity to generate wealth.
The DSSDE thus invites economists, policymakers, and data scientists to view inequality as the thermodynamic cost of social fragmentation. Reducing that cost is not charity—it is efficiency. Entropy is the hidden budget constraint of civilization.
The search for equilibrium has dominated economics for centuries. Yet equilibrium, as Polanyi warned, is a myth that conceals the violence of adjustment. True stability arises not from stasis but from dynamic balance— a living feedback between ethics and structure, between human intention and systemic design.
The Dynamic Structural Disequilibrium Economy is not an ending; it is a beginning. It sketches a path toward a discipline where mathematics and morality, data and dignity, belong to the same language.
The DSSDE system (A1–A12) can be implemented as a
dynamic simulation using R Shiny to visualize trajectories of
output, inequality, and entropy over time.
The app allows users to vary structural parameters and policy
instruments to explore nonlinear feedbacks and regime shifts.
Core parameters and controls:
| Symbol | Control | Description | Suggested Range |
|---|---|---|---|
| \(\alpha_0\) | Slider | Capital elasticity | 0.30–0.35 |
| \(\beta_0\) | Slider | Labor elasticity | 0.55–0.65 |
| \(\lambda_H\) | Slider | Entropy drag coefficient | 0.10–0.30 |
| \(\gamma_\pi,\gamma_V\) | Sliders | Inequality feedback coefficients | 0.05–0.15 |
| \(\tau_\eta\) | Slider | Fiscal progressivity | 0.10–0.40 |
| \(\mu\) | Slider | Financial–real interest gap | 0.00–0.10 |
Output panels:
A minimal implementation is available in the accompanying files
app.R and dssde_core.R, where all parameters
are initialized with the canonical calibration values derived from
Solow (1956), Prescott (1986), and Rodrik
(2011).
Users can modify any coefficient, rerun the simulation, and export
graphs for comparative analysis.
The code is open-source and designed for teaching, research, and policy
experimentation.
The entropy-drag hypothesis can be tested econometrically using cross-country or panel data.
Let \(H_t\) denote normalized Shannon entropy of income distribution:
\[ H_t = -\frac{1}{\log N}\sum_i p_{i,t}\log p_{i,t}, \] where \(p_{i,t}\) are income shares drawn from a log-normal distribution: \[ p_i \sim \text{LogNormal}(\mu_y,\sigma_y^2). \]
Estimate the regression: \[ \Delta\ln A_t = g_A - \lambda_H H_t + \varepsilon_t, \] where \(A_t\) is total-factor productivity, \(g_A\) the baseline growth rate, and \(\lambda_H\) the entropy-drag coefficient.
Formally:
\[ H_0: \lambda_H = 0 \quad \text{(no entropy effect)} \] \[ H_1: \lambda_H \ne 0 \quad \text{(entropy degrades productivity)}. \]
Testing can be implemented via nonlinear least squares or generalized method of moments, using datasets from Penn World Table, OECD, or World Inequality Database. Under a log-normal assumption, the null and alternative hypotheses fall in a skewed probability distribution rather than a normal one, reflecting the empirical asymmetry of income data.
Rejection of \(H_0\) confirms that structural entropy has a statistically significant effect on productivity decline.
Figure A1 illustrates the feedback structure of the DSSDE. For clarity, it can be rendered in TikZ or any diagramming package using the following description.
Nodes:
Arrows (causal direction):
\[ \eta \rightarrow H(\eta) \rightarrow (\alpha,\beta,A) \rightarrow Y \rightarrow (\tau,G,\mu) \rightarrow \eta. \]
Interpretation:
The loop represents a self-organizing, non-linear dynamic in which inequality raises entropy; entropy lowers productivity; and policy interventions can dampen or amplify this cycle depending on their moral and institutional design.
A minimalist TikZ implementation for rendering within the R Markdown environment is given below:
The complete R code, datasets, and supplementary documentation are available upon request. All parameters may be altered for sensitivity analysis. The simulation is designed to be falsifiable, educational, and extendable to agent-based or system-dynamics frameworks.
By integrating quantitative modeling with moral inquiry, the DSSDE appendix reaffirms that reproducibility in economics must include not only data transparency but also ethical transparency.
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