EconAgentic: LLM Framework for Decentralized Physical Infrastructure Markets

Teburin Abubuwan Ciki

• 1 Gabatarwa
• 2 Tsarin EconAgentic
  • 2.1 Architecture Overview
  • 2.2 Multi-Agent System Design
• 3 Aiwatar da Fasaha
  • 3.1 Samfurori na Lissafi
  • 3.2 Code Implementation
• 4 Experimental Results
  • 4.1 Simulation Setup
  • 4.2 Performance Metrics
• 5 Analysis and Insights
• 6 Aikace-aikace na Gaba
• 7 Nassoshi

1 Gabatarwa

Decentralized Physical Infrastructure (DePIN) yana da wata hanya mai canzawa don sarrafa kadarorin jiki ta hanyar fasahar blockchain. Ya zuwa 2024, ayyukan DePIN sun wuce dala biliyan 10 a cikin darajar kasuwa, wanda ke nuna saurin karɓuwa. Duk da haka, aikin 'yan adawar AI da ke cikin waɗannan kasuwannin da ba a haɗa su ba yana haifar da haɗarin rashin inganci da kuma rashin daidaito da ƙimar ɗan adam. Wannan takarda ta gabatar da EconAgentic, wani tsari mai amfani da LLM wanda aka ƙera don ƙira, kimantawa, da haɓaka kasuwannin DePIN.

2 Tsarin EconAgentic

EconAgentic framework yana amfani da Manyan Samfuran Harshe don kwaikwayi yanayin kasuwar DePIN da hulɗar masu ruwa da tsaki.

2.1 Architecture Overview

Tsarin ya ƙunshi manyan sassa uku na asali: injin kwaikwayon kasuwa, ƙirar halayen wakili, da na'urar binciken tasirin tattalin arziki. Tsarin ginin yana haɗuwa da sahihancin hanyoyin sadarwa na blockchain kamar Ethereum da Solana ta hanyar hanyoyin haɗin kwangila mai wayo.

2.2 Multi-Agent System Design

Wakilai suna wakiltar masu ruwa da tsaki daban-daban: masu ba da kayayyakin more rayuwa, masu riƙe token, da mahalarta mulki. Kowane nau'in wakili yana da maƙasudai daban-daban da hanyoyin yanke shawara waɗanda aka ƙirƙira ta hanyar tunanin LLM.

3 Aiwatar da Fasaha

3.1 Samfurori na Lissafi

Tsarin yana amfani da koyarwar ƙarfafawa don inganta yanke shawara na wakili. Aikin lada ga masu samar da kayayyakin more rayuwa an ayyana shi kamar haka: $R_t = \sum_{i=1}^n \gamma^i r_{t+i} + \lambda \cdot T_t$ inda $R_t$ ya zama lada gabaɗaya, $\gamma$ shine ma'aunin rangwame, $r_{t+i}$ shine lada nan take, kuma $T_t$ yana wakiltar ƙarfafa alama.

Ma'auni na kasuwa an ƙirƙira shi ta amfani da: $Q_d(P) = \alpha - \beta P + \delta A$ da $Q_s(P) = \theta + \phi P - \psi C$ inda $Q_d$ shine adadin da ake buƙata, $Q_s$ shine adadin da aka samar, $P$ shine farashi, $A$ yana wakiltar ayyukan wakilin AI, kuma $C$ yana nuna farashin kayan aiki.

3.2 Code Implementation

class DePINAgent:

4 Experimental Results

4.1 Simulation Setup

Mun yi simulation DePIN kasuwa tare da wakilai 1000 a cikin watanni 6 na lokaci na zahiri. Yanayin ya haɗa da canjin farashin token, buƙatun ababen more rayuwa, da tsarukan haɓakar cibiyar sadarwa.

4.2 Performance Metrics

Sakamako mai mahimmanci ya nuna kasuwannin da AI ke tafiyar da su sun sami ingantaccen inganci na kashi 30% a cikin rabon albarkatu idan aka kwatanta da hanyoyin dabarun ɗan adam. Canjin farashin Token ya ragu da kashi 45% a cikin yanayin da AI ta inganta, yayin da amfani da kayan aikin more rayuwa ya inganta da kashi 28%.

5 Analysis and Insights

The EconAgentic framework represents a significant advancement in decentralized market simulation, bridging the gap between theoretical tokenomics and practical implementation. Unlike traditional economic models that rely on simplified assumptions of rational actors, this approach captures the complex, emergent behaviors in DePIN ecosystems through LLM-powered agents capable of nuanced decision-making. The integration of reinforcement learning with economic modeling follows similar approaches seen in advanced AI systems like those described in the CycleGAN paper (Zhu et al., 2017), where adversarial training improves system performance through competitive optimization.

Our findings align with research from institutions like the Stanford Blockchain Research Center, which emphasizes the importance of simulation in understanding complex decentralized systems. The 30% efficiency improvement observed in AI-driven markets demonstrates the potential for LLM agents to optimize resource allocation beyond human capabilities, particularly in high-dimensional decision spaces. However, this also raises important questions about value alignment, as noted in research from the Future of Humanity Institute at Oxford, which warns about the risks of autonomous systems operating without proper ethical constraints.

Tsarin lissafi ya ginu akan kafaffen ka'idar tattalin arziki yayin haɗa sababbin abubuwa na musamman ga tattalin arzikin tushen alama. Ƙirar aikin lada tana nuna kamanceceniya da hanyoyin a cikin binciken koyo mai zurfi daga DeepMind, musamman a yadda ake daidaita ƙimar dogon lokaci da lada nan take. Ma'auni na ma'auni na kasuwa yana ƙaddamar da ƙirar wadata da buƙata ta al'ada ta hanyar haɗa ayyukan wakilin AI a matsayin ma'auni na bayyane, yana yarda da girma tasirin masu halartar kai tsaye a kasuwannin dijital.

A gaba da nan, ka'idojin da aka nuna a cikin EconAgentic na iya yin tasiri ga faffadan aikace-aikace a cikin kuɗin rarrabawa da sarrafa kasuwa ta atomatik. Nasarar wannan hanyar ta nuna cewa simintin LLM mai ƙarfi zai iya zama daidaitaccen kayan aiki don ƙira da gwada hanyoyin tattalin arziki a cikin yanayin Web3, kamar yadda ƙididdigar ƙididdiga ta canza ƙirar injiniya. Koyaya, dole ne a mai da hankali sosai ga hanyoyin gudanarwa don tabbatar da cewa waɗannan tsarin sun kasance daidai da ƙimar ɗan adam yayin da suke haɓakawa.

6 Aikace-aikace na Gaba

Tsarin EconAgentic yana da yuwuwar aikace-aikace bayan kasuwannin DePIN, gami da ƙirar yarjejeniya na kuɗi mai rarrabawa (DeFi), inganta tattalin arzikin alama, da gwajin yarda da ka'idoji. Aikin gaba zai mai da hankali kan haɗin kai tsakanin sarkaki, sa ido kan kasuwa na ainihi, da haɗin kai tare da na'urorin IoT don sarrafa kayayyakin more rayuwa na jiki. Hakanan za a iya daidaita tsarin don kwaikwayon kuɗin banki na tsakiya da tasirinsu akan tsarin kuɗi na gargajiya.

7 Nassoshi

1. Zhu, J. Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. IEEE International Conference on Computer Vision.
2. Buterin, V. (2014). A Next-Generation Smart Contract and Decentralized Application Platform. Ethereum White Paper.
3. Nakamoto, S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System.
4. Bonneau, J., Miller, A., Clark, J., Narayanan, A., Kroll, J. A., & Felten, E. W. (2015). SoK: Research Perspectives and Challenges for Bitcoin and Cryptocurrencies. IEEE Symposium on Security and Privacy.
5. Catalini, C., & Gans, J. S. (2016). Some Simple Economics of the Blockchain. NBER Working Paper.