Algorithmic Trust and the Future of Money: A Dynamic General Equilibrium Framework for AI-Governed Digital Currencies
Arva Athallah Susanto
Dalhousie University // Visiting Graduate Research Student
Abstract
This paper develops a new theoretical framework for understanding the future of money in an economy where artificial intelligence, institutional governance, and moral preferences jointly shape monetary stability. We introduce the General Moral-Algorithmic Equilibrium (GMAE), a dynamic general equilibrium model in which digital money operates as a programmable computational object governed by an AI-based policy function. Unlike traditional monetary frameworks where trust is implicit and money is passive, our model treats trust as an endogenous state variable, evolving through transparency, algorithmic performance, and institutional leakage. We show that macroeconomic stability in AI-mediated monetary systems depends not only on economic fundamentals but on the moral-algorithmic architecture underlying digital currency design. High transparency, ethical programmability, and low leakage generate a stable high-trust equilibrium with dampened volatility. Conversely, opacity, coercive programmability, or algorithmic overreaction produce instability, dual equilibria, or trust collapse—even absent economic shocks. Policy simulations demonstrate that AI-governed digital money can outperform conventional rules when aligned with societal values but becomes a source of systemic risk when moral alignment fails. Our findings imply that the future of money will be shaped as much by algorithmic legitimacy and moral capital as by traditional macroeconomic instruments. GMAE offers a unified foundation for designing safe, transparent, and ethically coherent digital monetary institutions.
I. Pengantar & Pergeseran Kepercayaan (Demystifying Monetary Commons)
Riset ini menjembatani dua kutub pemikiran yang seringkali terpisah: antropologi ekonomi tentang uang (seperti teori monetary commons dan legitimasi institusional oleh Prof. Daromir Rudnyckyj) dan makroekonomi kuantitatif modern. Penulis berpendapat bahwa uang digital, khususnya Central Bank Digital Currency (CBDC), bukan sekadar alat transaksi yang efisien, melainkan perubahan mendasar dalam struktur kepercayaan masyarakat.
Dalam sistem fiat tradisional, kepercayaan publik dialamatkan secara buta kepada institusi manusia (Bank Sentral). Pada sistem GMAE, kepercayaan dialihkan menjadi Algorithmic Trust yang diprogram secara eksplisit dan dipantau secara transparan oleh publik, mendemokratisasikan pembuatan kebijakan moneter dari ruang rapat tertutup menjadi ledger komputasi publik.
II. Kerangka Kerja Matematis GMAE (The Core Equations)
GMAE memformulasikan tingkat Kepercayaan Publik ($T_t$) sebagai variabel keadaan dinamis (endogenous state variable) yang berevolusi berdasarkan persamaan diferensial diskret berikut:
Dimana parameter pembentuknya didefinisikan sebagai:
- Transparency ($tp$): Tingkat keterbukaan kode pemrograman, audit algoritma emisi uang, dan keterbukaan operasional ledger.
- Leakage ($lk$): Kerentanan keamanan, penyalahgunaan wewenang kontrol ledger, korupsi kelembagaan, atau inflasi liar.
- Moral Alignment ($\alpha$): Keselarasan aturan terprogram (misalnya pembatasan penggunaan, kriteria kepatuhan hijau) dengan konsensus nilai etis & norma sosial masyarakat.
III. Analisis Regim Stabilitas & Kolaps Moral (Stability Regimes)
Melalui kalkulasi Matriks Jacobian 2D, model membuktikan keberadaan regim keseimbangan ganda (dual equilibria). Jika tingkat transparansi berada di bawah batas kritis ($tp < tp^*$) atau tingkat kebocoran terlalu tinggi ($lk > lk^*$), sistem akan bergeser dari High-Trust Stability Regime menuju Moral-Algorithmic Collapse Regime.
Pada regim kolaps ini, kepatuhan masyarakat hancur, likuiditas moneter menyusut drastis, dan inflasi melonjak secara eksponensial. Menariknya, kolaps ini dapat terjadi murni akibat hilangnya legitimasi algoritma (etika pemrograman uang yang opresif atau buruk) bahkan tanpa adanya guncangan ekonomi eksternal sekalipun.
IV. Kebijakan Moneter Otonom (AI-Governed Autopilot)
Sebagai solusi penyeimbang, paper menyimulasikan AI-Governed Policy Function yang berfungsi sebagai pengendali umpan balik aktif (Proportional-Integral-Derivative Controller). Sistem otonom ini secara dinamis menyetel tingkat suku bunga kebijakan ($r_t$) dan suplai uang ($M_t$) secara instan berdasarkan sinyal sensor inflasi ($\pi_t$) dan kepercayaan publik ($T_t$).
Hasil simulasi menunjukkan bahwa kebijakan otonom berbasis AI mampu menstabilkan volatilitas siklus ekonomi lebih cepat dibanding aturan statis (seperti Taylor Rule konvensional), asalkan parameter moral alignment ($\alpha$) terkunci secara sinkron di tingkat tinggi.
Pertanyaan Umum & Jawaban Kunci (FAQs)
The study reveals that higher algorithmic transparency enhances compliance and reduces liquidity volatility, thus increasing financial stability. Specifically, perceived improvements in transparency can stimulate algorithmic trust elasticity, significantly benefiting overall monetary resilience during economic shocks.
Moral preferences are identified as crucial structural components, impacting liquidity and compliance in digital economies. In societies with strong moral capital, ethical constraints enhance trust, whereas coercive perceptions lead to decreased legitimacy, ultimately affecting economic stability.
Over 130 central banks worldwide have started exploring CBDCs with programmability since 2023, indicating a significant shift in monetary governance. This movement towards programmable money integrates algorithmic rules that are responsive to macroeconomic signals.
GMAE integrates algorithmic trust, institutional integrity, and societal moral preferences into a dynamic equilibrium model. This framework posits that stability in digital economies emerges from the co-evolution of these factors rather than traditional economic fundamentals alone.
Crises can arise from factors such as algorithmic opacity and ethical misalignment, rather than solely from economic shocks. The framework illustrates that trust dynamics can lead to dual equilibria: a stable, high-trust environment or an unstable, low-trust regime.