# Can an Agent-Based Model Be Utilized to Detect Collusive Behavior among Responding Dealers in an Rfq Auction? ▴ Question

**Published:** 2025-08-04
**Categories:** Question

---

![A luminous digital market microstructure diagram depicts intersecting high-fidelity execution paths over a transparent liquidity pool. A central RFQ engine processes aggregated inquiries for institutional digital asset derivatives, optimizing price discovery and capital efficiency within a Prime RFQ](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocols-high-fidelity-execution-principal-operational-framework-price-discovery.jpg)

![A precision-engineered apparatus with a luminous green beam, symbolizing a Prime RFQ for institutional digital asset derivatives. It facilitates high-fidelity execution via optimized RFQ protocols, ensuring precise price discovery and mitigating counterparty risk within market microstructure](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-rfq-protocol-high-fidelity-digital-asset-derivatives-execution.jpg)

## Concept

The architecture of institutional trading rests on protocols designed for specific outcomes. The Request for Quote (RFQ) system, a cornerstone of over-the-counter (OTC) and block trading, is engineered for discreet, efficient [price discovery](https://prime.greeks.live/area/price-discovery/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) on large or illiquid positions. Its structure, which directs an inquiry to a select group of liquidity providers, creates a contained, competitive environment. This very containment, however, produces the precise conditions under which collusive arrangements can form.

The challenge is not one of abstract market failure; it is a question of structural integrity. An [Agent-Based Model](https://prime.greeks.live/area/agent-based-model/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) (ABM) provides the necessary tool to examine this integrity. It functions as a digital twin of the RFQ ecosystem, a simulated environment where the behavioral dynamics of market participants can be modeled, tested, and understood. By creating autonomous agents that replicate the decision-making logic of both competitive and potentially collusive dealers, an ABM allows for the systematic analysis of how illicit coordination might manifest within the data signatures of the [RFQ process](https://prime.greeks.live/area/rfq-process/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) itself.

The fundamental premise is that all market actions, whether legitimate or coordinated, leave a footprint. Competitive quoting leaves a pattern of rational, self-interested behavior dictated by factors like inventory, risk models, and perceived market volatility. Collusive quoting, while designed to appear legitimate, must deviate from this baseline in subtle but measurable ways. These deviations are the signals an ABM is designed to detect.

The model moves beyond static statistical analysis of past trades. It creates a dynamic simulation where the “rules of the game” can be precisely defined and manipulated. This allows an institution to ask critical questions ▴ Under what conditions does collusion become a probable strategy for a group of dealers? What are the specific data patterns that emerge when it does?

How would our own trading footprint look to a collusive ring? The ABM, therefore, becomes a laboratory for understanding systemic vulnerabilities.

> An Agent-Based Model serves as a high-fidelity simulation of the RFQ market, enabling the identification of collusive behaviors by analyzing deviations from expected competitive patterns.

This approach provides a profound operational advantage. It transforms the detection of collusion from a reactive, forensic exercise into a proactive, systemic capability. The goal is to understand the physics of the system, the interplay of incentives and information that governs [dealer behavior](https://prime.greeks.live/area/dealer-behavior/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite). An ABM allows a firm to model various collusive strategies ▴ such as designated winners, bid suppression, or market division ▴ and observe their impact on the simulated market.

The output is not a simple “collusion/no collusion” flag. The output is a detailed map of the system’s pressure points and a set of data-driven indicators that can be integrated into a firm’s real-time monitoring and compliance frameworks. The value lies in building a deeper, mechanistic understanding of the trading environment, which is the foundational layer of superior execution and risk management.

![A sleek, futuristic apparatus featuring a central spherical processing unit flanked by dual reflective surfaces and illuminated data conduits. This system visually represents an advanced RFQ protocol engine facilitating high-fidelity execution and liquidity aggregation for institutional digital asset derivatives](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-digital-asset-options-rfq-liquidity-aggregation-interface.jpg)

![An abstract visualization of a sophisticated institutional digital asset derivatives trading system. Intersecting transparent layers depict dynamic market microstructure, high-fidelity execution pathways, and liquidity aggregation for RFQ protocols](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-institutional-digital-asset-derivatives-rfq-execution.jpg)

## Strategy

The strategic implementation of an Agent-Based Model for [collusion detection](https://prime.greeks.live/area/collusion-detection/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) requires a precise architectural plan. The model’s efficacy is a direct function of how accurately it represents the key actors and their decision-making frameworks within the RFQ process. The core strategy involves constructing a simulated market environment populated by agents whose behaviors are governed by a set of logical rules, allowing for the isolation and identification of collusive tactics.

![A central, metallic hub anchors four symmetrical radiating arms, two with vibrant, textured teal illumination. This depicts a Principal's high-fidelity execution engine, facilitating private quotation and aggregated inquiry for institutional digital asset derivatives via RFQ protocols, optimizing market microstructure and deep liquidity pools](https://options.greeks.live/wp-content/uploads/2025/07/prime-brokerage-digital-asset-rfq-execution-engine-microstructure.jpg)

## Defining the Agent Population

The simulation requires a minimum of two agent types to replicate the fundamental RFQ interaction. Each agent is a distinct software entity with its own set of parameters and behavioral logic.

- 
**Client Agent** This agent initiates the RFQ process. Its primary role is to generate quote requests based on a set of predefined or stochastic criteria, such as instrument type, trade size, and frequency. The Client Agent is the catalyst for market activity.

- 
**[Dealer Agents](https://prime.greeks.live/question/to-what-extent-can-machine-learning-techniques-enhance-the-behavioral-realism-of-simulated-market-agents/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite)** These agents represent the liquidity providers who respond to the RFQ. This population must be further segmented to model the core conflict between competition and collusion. A subset of dealer agents will be programmed to act competitively, while another subset will be programmed with collusive logic.

![A central toroidal structure and intricate core are bisected by two blades: one algorithmic with circuits, the other solid. This symbolizes an institutional digital asset derivatives platform, leveraging RFQ protocols for high-fidelity execution and price discovery](https://options.greeks.live/wp-content/uploads/2025/07/digital-asset-derivatives-rfq-execution-hub-integrating-algorithmic-intelligence-for-high-fidelity-price-discovery-and-liquidity-aggregation.jpg)

## Modeling Agent Behavior and Decision Logic

The central pillar of the strategy is the codification of [agent behavior](https://prime.greeks.live/question/how-can-an-agent-based-model-improve-the-accuracy-of-simulating-dealer-behavior-in-an-rfq-backtest/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite). The rules governing dealer agents must be grounded in the economic realities of market making. This is where the model gains its analytical power, by creating a baseline of expected behavior against which anomalies can be measured.

![Interlocking geometric forms, concentric circles, and a sharp diagonal element depict the intricate market microstructure of institutional digital asset derivatives. Concentric shapes symbolize deep liquidity pools and dynamic volatility surfaces](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocols-and-high-fidelity-execution.jpg)

## Competitive Dealer Logic

A competitive dealer agent’s quoting behavior is a function of several internal and external variables. Its goal is to win the auction at a price that compensates it for risk and cost, within a competitive spread. Key inputs to its pricing engine include:

- 
**Internal [Fair Value](https://prime.greeks.live/area/fair-value/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite)** Each dealer maintains its own internal model for the “true” price of the instrument.

- 
**Inventory Position** A dealer long the instrument will be more aggressive in its offer (sell) price, while a dealer short the instrument will be more aggressive on its bid (buy) price.

- 
**Risk Parameters** This includes the dealer’s tolerance for risk, current VaR (Value at Risk), and other risk management metrics.

- 
**Operational Costs** The cost of executing and settling the trade.

- 
**Perceived Competition** The number of other dealers invited to the RFQ. More dealers should, in theory, lead to tighter spreads.

![An intricate, transparent digital asset derivatives engine visualizes market microstructure and liquidity pool dynamics. Its precise components signify high-fidelity execution via FIX Protocol, facilitating RFQ protocols for block trade and multi-leg spread strategies within an institutional-grade Prime RFQ](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-digital-asset-derivatives-rfq-liquidity-ecosystem.jpg)

## Collusive Dealer Logic

Collusive agents operate with an additional layer of logic. They still possess the underlying competitive pricing engine, but their actions are modified by a shared agreement with other colluding agents. The ABM can model several forms of collusion identified in market analysis.

One common tactic is the “[designated winner](https://prime.greeks.live/area/designated-winner/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite)” scheme. In this scenario, members of the cartel tacitly agree which dealer will win a particular auction. The designated winner submits a bid designed to maximize profit, while the other colluding dealers submit intentionally non-competitive “cover” quotes. These quotes serve to create the illusion of a competitive auction.

Another strategy is market division, where dealers agree not to compete for certain types of trades or clients, effectively granting each other mini-monopolies. The ABM can simulate these behaviors by programming a “collusion protocol” that allows designated agents to coordinate their quoting strategy before responding to the RFQ.

![A sophisticated institutional digital asset derivatives platform unveils its core market microstructure. Intricate circuitry powers a central blue spherical RFQ protocol engine on a polished circular surface](https://options.greeks.live/wp-content/uploads/2025/08/institutional-digital-asset-derivatives-prime-os-unveiling-rfq-protocol-engine-market-microstructure-for-optimal-price-discovery-and-high-fidelity-execution.jpg)

## Data Inputs and Detection Metrics

The model is fueled by data and produces metrics that flag suspicious activity. The primary input is historical [RFQ data](https://prime.greeks.live/area/rfq-data/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite), which provides the statistical foundation for calibrating agent behavior. The output is a set of indicators that measure deviations from the competitive baseline.

> By establishing a clear baseline for competitive quoting, the model can effectively isolate and flag the statistical anomalies produced by coordinated collusive actions.

The table below outlines some of the key metrics the ABM would be designed to track. Each metric provides a different lens through which to view the auction’s integrity.

### Table 1 ▴ Key Collusion Detection Metrics

| Metric | Description | Collusive Signature |
| --- | --- | --- |
| Quote Dispersion | The standard deviation of all quotes received for an RFQ. | An unusually high dispersion, where the winning quote is an outlier and other quotes are clustered far away. |
| Winner’s Price vs. Fair Value | The difference between the winning quote and the model’s calculated fair value at the time of the auction. | A consistently high profit margin for the winning dealer, especially when the winner is part of a recurring group of participants. |
| Response Time Correlation | Analysis of the timing of quote submissions. | Anomalous patterns, such as colluding dealers waiting for the designated winner to quote first before submitting their cover quotes in rapid succession. |
| Win-Loss Pattern Analysis | Tracking which dealers win auctions and which other dealers participated in those same auctions. | A non-random pattern where wins are rotated among a specific group of dealers, or where one dealer consistently wins when a certain set of other dealers are present. |
| Quote Symmetry | In a Request for Market (RFM) where dealers provide a two-sided quote, this measures the symmetry of the spread around the perceived mid-price. | Colluding dealers might provide artificially wide or skewed spreads to discourage the client, allowing the designated winner’s more aggressive quote to stand out. |

By simulating thousands or millions of RFQ auctions under different scenarios, the system can learn the statistical properties of a competitive market. When the collusive agents are activated, the model can then identify which metrics are the most reliable indicators of illicit coordination. This strategic framework provides a robust, evidence-based system for moving beyond suspicion and toward the data-driven detection of collusive behavior.

![Abstractly depicting an institutional digital asset derivatives trading system. Intersecting beams symbolize cross-asset strategies and high-fidelity execution pathways, integrating a central, translucent disc representing deep liquidity aggregation](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-ecosystem-unifying-rfq-protocols-for-high-fidelity-execution-and-deep-liquidity-aggregation-across-multi-leg-spreads.jpg)

![A central precision-engineered RFQ engine orchestrates high-fidelity execution across interconnected market microstructure. This Prime RFQ node facilitates multi-leg spread pricing and liquidity aggregation for institutional digital asset derivatives, minimizing slippage](https://options.greeks.live/wp-content/uploads/2025/08/high-fidelity-rfq-execution-for-institutional-crypto-derivatives.jpg)

## Execution

The execution of an Agent-Based Model for collusion detection moves from strategic design to operational implementation. This phase involves constructing the simulation environment, defining the quantitative models that drive agent behavior, and establishing a rigorous process for analyzing the output. The objective is to create a working system that can generate actionable intelligence for compliance and trading teams.

![A bifurcated sphere, symbolizing institutional digital asset derivatives, reveals a luminous turquoise core. This signifies a secure RFQ protocol for high-fidelity execution and private quotation](https://options.greeks.live/wp-content/uploads/2025/08/institutional-digital-asset-derivatives-rfq-protocol-orchestration.jpg)

## The Operational Playbook

Deploying a functional ABM requires a clear, step-by-step process. This playbook outlines the core stages of building, calibrating, and utilizing the model for collusion detection.

- 
**Data Aggregation and Cleansing** The first step is to gather all available historical RFQ data. This includes timestamps, instrument identifiers, trade sizes, the list of invited dealers for each RFQ, the full set of quotes received (if available), and the identity of the winning dealer. This data must be cleansed and standardized to serve as the foundation for the model.

- 
**Environment Construction** The next step is to build the core simulation software. This involves creating the “market” object, which manages the flow of time and communication between agents. It also involves developing the agent classes (Client, Dealer) with their associated attributes and methods.

- 
**Competitive Baseline Calibration** Using the historical data, the model’s competitive dealer agents are calibrated. This involves statistically analyzing past quoting behavior to set realistic parameters for variables like average spread, response to volatility, and inventory effects. The goal is to create a simulation that, in its “competitive-only” mode, produces market data that is statistically indistinguishable from real-world observations.

- 
**Implementation of Collusive Scenarios** With the baseline established, specific collusive strategies are programmed into a subset of dealer agents. This could start with a simple “designated winner” algorithm and evolve to more complex market division or signaling strategies. Each strategy is a distinct module that can be turned on or off.

- 
**Simulation and Data Generation** The model is then run for a large number of iterations. Each run simulates a period of market activity (e.g. a day, a week) and outputs a detailed log of all RFQs, quotes, and agent states. This process is repeated for different scenarios ▴ a purely competitive market, and markets with varying numbers of colluding agents and different collusive strategies.

- 
**Analysis and Signature Identification** The data generated from the competitive and collusive simulations are compared. Statistical analysis is performed to identify which of the detection metrics (as defined in the Strategy section) are the most effective at discriminating between the two states. This identifies the “fingerprints” of collusion.

- 
**Integration with Monitoring Systems** The final step is to translate the findings into production tools. This could involve building a “collusion risk” dashboard that scores real-world RFQ activity against the identified signatures in real-time, flagging high-risk auctions for human review.

![Metallic hub with radiating arms divides distinct quadrants. This abstractly depicts a Principal's operational framework for high-fidelity execution of institutional digital asset derivatives](https://options.greeks.live/wp-content/uploads/2025/07/digital-asset-derivatives-rfq-execution-prime-os-market-microstructure.jpg)

## Quantitative Modeling and Data Analysis

The core of the execution phase lies in the quantitative definition of agent behavior and the analysis of the data the simulation produces. The following tables illustrate the level of detail required.

![A conceptual image illustrates a sophisticated RFQ protocol engine, depicting the market microstructure of institutional digital asset derivatives. Two semi-spheres, one light grey and one teal, represent distinct liquidity pools or counterparties within a Prime RFQ, connected by a complex execution management system for high-fidelity execution and atomic settlement of Bitcoin options or Ethereum futures](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-market-microstructure-2.jpg)

## How Are Dealer Agent Profiles Constructed?

Each dealer agent is parameterized to create a diverse and realistic population of liquidity providers. This prevents simplistic modeling and ensures the simulation captures a range of behaviors.

### Table 2 ▴ Example Dealer Agent Parameterization

| Parameter | Agent A (Aggressive) | Agent B (Standard) | Agent C (Risk Averse) | Agent D (Colluder) |
| --- | --- | --- | --- | --- |
| Base Spread (bps) | 0.5 | 1.0 | 2.0 | 1.0 (when competitive) |
| Inventory Cost Factor | Low | Medium | High | Medium |
| Volatility Multiplier | 1.2x | 1.5x | 2.0x | 1.5x |
| Collusion Flag | False | False | False | True |
| Collusion Strategy | N/A | N/A | N/A | Designated Winner |

![A metallic precision tool rests on a circuit board, its glowing traces depicting market microstructure and algorithmic trading. A reflective disc, symbolizing a liquidity pool, mirrors the tool, highlighting high-fidelity execution and price discovery for institutional digital asset derivatives via RFQ protocols and Principal's Prime RFQ](https://options.greeks.live/wp-content/uploads/2025/08/institutional-digital-asset-derivatives-rfq-protocols-driving-high-fidelity-execution-and-market-microstructure-optimization-via-principal-prime-os.jpg)

## Predictive Scenario Analysis

To illustrate the model’s utility, consider a hypothetical RFQ for a corporate bond. A client sends an RFQ for $10 million of a specific bond to five dealers. The model’s internal fair value for the bond is 100.00. We will run this scenario twice ▴ once with all dealers acting competitively, and once where Dealers D and E are colluding, with Dealer D being the designated winner.

In the competitive scenario, all five dealers calculate their quotes based on their internal parameters. The aggressive dealer, Agent A, provides the tightest quote to win the business. The quotes are tightly clustered, reflecting a healthy, competitive tension. The winning price is only slightly above the fair value, representing a small but fair profit for the winning dealer.

In the collusive scenario, the logic changes. Dealer D, the designated winner, knows it faces less genuine competition. It submits a quote significantly wider than it would in a competitive auction, for example, 100.15. Dealer E, its co-conspirator, understands its role is to provide a cover quote.

It submits a deliberately uncompetitive quote, such as 100.25, to give the appearance of participation without threatening Dealer D’s position. The other competitive dealers, A, B, and C, submit their normal quotes. However, Dealer D’s artificially high quote still wins the auction because it has effectively eliminated one source of genuine competition (Dealer E). The result is a higher price for the client and a supra-competitive profit for Dealer D.

![A luminous central hub with radiating arms signifies an institutional RFQ protocol engine. It embodies seamless liquidity aggregation and high-fidelity execution for multi-leg spread strategies](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-hub-for-optimal-liquidity-aggregation.jpg)

## System Integration and Technological Architecture

For the ABM to be more than a research tool, it must be integrated into a firm’s operational infrastructure. The architecture would typically consist of several components:

- 
**Data Ingestion Engine** A service that connects to the firm’s trading and RFQ systems (e.g. via FIX protocol logs or database replication) to pull in real-time and historical RFQ data.

- 
**Simulation Core** The ABM engine itself, likely built in a language like Python or Java, using specialized libraries for agent-based modeling. This core runs the simulations based on the ingested data and programmed logic.

- 
**Analytics Database** A database (e.g. a time-series database or a columnar store) designed to hold the massive volumes of output data from the simulations.

- 
**Alerting and Visualization Layer** A front-end application, such as a web-based dashboard, that queries the analytics database. This layer would display the collusion risk scores for recent RFQs, highlight anomalous patterns, and allow compliance officers to drill down into the data for specific auctions that have been flagged.

This systematic execution provides a powerful, evidence-based framework for identifying and acting upon the subtle signals of collusive behavior within RFQ markets, turning a complex detection problem into a manageable, data-driven workflow.

![A precise stack of multi-layered circular components visually representing a sophisticated Principal Digital Asset RFQ framework. Each distinct layer signifies a critical component within market microstructure for high-fidelity execution of institutional digital asset derivatives, embodying liquidity aggregation across dark pools, enabling private quotation and atomic settlement](https://options.greeks.live/wp-content/uploads/2025/07/principal-digital-asset-rfq-protocol-layers-for-high-fidelity-execution.jpg)

## References

- Calvano, Emilio, et al. “Algorithmic Collusion ▴ What It Is and What It Is Not.” Competition Policy International, 2020.

- Clark, Robert, et al. “Investigating Cartels in the Digital Era ▴ A Guide for Competition Authorities.” Journal of Competition Law & Economics, vol. 16, no. 2, 2020, pp. 123-167.

- Conley, Timothy G. and Francesco Decarolis. “Detecting Bidding Rings in Sequential Auctions.” American Economic Journal ▴ Microeconomics, vol. 8, no. 1, 2016, pp. 1-35.

- Ezrachi, Ariel, and Maurice E. Stucke. Virtual Competition ▴ The Promise and Perils of the Algorithm-Driven Economy. Harvard University Press, 2016.

- Massarotto, Giovanna. “Antitrust Needs to Draw on Computer Science to Detect Algorithmic Collusion.” ProMarket, University of Chicago Booth School of Business, 23 Jan. 2024.

- Musolff, Leon. “Detection of Collusive Networks in E-Procurement.” SSRN Electronic Journal, 2024.

- Stoikov, Sasha. “The Microstructure of High-Frequency Trading.” The Journal of Trading, vol. 7, no. 4, 2012, pp. 23-31.

- Vytelingum, Perukrishnen. “Agent-Based Micro-simulation of Financial Markets.” The Knowledge Engineering Review, vol. 21, no. 2, 2006, pp. 111-131.

![Sleek, two-tone devices precisely stacked on a stable base represent an institutional digital asset derivatives trading ecosystem. This embodies layered RFQ protocols, enabling multi-leg spread execution and liquidity aggregation within a Prime RFQ for high-fidelity execution, optimizing counterparty risk and market microstructure](https://options.greeks.live/wp-content/uploads/2025/08/institutional-digital-asset-derivatives-rfq-prime-os-architecture.jpg)

## Reflection

The implementation of an Agent-Based Model represents a shift in perspective. It moves the analysis of market integrity from a post-mortem examination of individual trades to a continuous, systemic assessment of the trading environment itself. The knowledge gained from such a system is a component in a larger architecture of institutional intelligence.

The true capability is not merely flagging a suspicious auction; it is the deeper understanding of the market’s mechanics that allows a firm to refine its execution protocols, select its counterparties with greater confidence, and ultimately, operate with a structural advantage. The question then becomes ▴ How is your operational framework architected to perceive and adapt to the subtle, systemic risks within your own trading ecosystem?

![Luminous blue drops on geometric planes depict institutional Digital Asset Derivatives trading. Large spheres represent atomic settlement of block trades and aggregated inquiries, while smaller droplets signify granular market microstructure data](https://options.greeks.live/wp-content/uploads/2025/07/institutional-grade-digital-asset-derivatives-rfq-protocols-driving-high-fidelity-execution-and-optimal-liquidity-discovery.jpg)

## Glossary

<a href="https://prime.greeks.live/area/price-discovery/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![An exposed high-fidelity execution engine reveals the complex market microstructure of an institutional-grade crypto derivatives OS. Precision components facilitate smart order routing and multi-leg spread strategies](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-execution-engine-microstructure-2.jpg)

## Price Discovery

Meaning ▴ Price Discovery, within the context of crypto investing and market microstructure, describes the continuous process by which the equilibrium price of a digital asset is determined through the collective interaction of buyers and sellers across various trading venues. </a><a href="https://prime.greeks.live/area/agent-based-model/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![Intricate dark circular component with precise white patterns, central to a beige and metallic system. This symbolizes an institutional digital asset derivatives platform's core, representing high-fidelity execution, automated RFQ protocols, advanced market microstructure, the intelligence layer for price discovery, block trade efficiency, and portfolio margin](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-core.jpg)

## Agent-Based Model

Meaning ▴ An Agent-Based Model (ABM) is a computational framework that simulates the actions and interactions of autonomous agents within an environment to observe the emergence of complex system-wide behaviors. </a><a href="https://prime.greeks.live/area/rfq-process/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![An abstract composition featuring two overlapping digital asset liquidity pools, intersected by angular structures representing multi-leg RFQ protocols. This visualizes dynamic price discovery, high-fidelity execution, and aggregated liquidity within institutional-grade crypto derivatives OS, optimizing capital efficiency and mitigating counterparty risk](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocols-optimizing-aggregated-liquidity-and-multi-leg-execution-via-advanced-market-microstructure.jpg)

## Rfq Process

Meaning ▴ The RFQ Process, or Request for Quote process, is a formalized method of obtaining bespoke price quotes for a specific financial instrument, wherein a potential buyer or seller solicits bids from multiple liquidity providers before committing to a trade. </a><a href="https://prime.greeks.live/area/dealer-behavior/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A sleek, modular institutional grade system with glowing teal conduits represents advanced RFQ protocol pathways. This illustrates high-fidelity execution for digital asset derivatives, facilitating private quotation and efficient liquidity aggregation](https://options.greeks.live/wp-content/uploads/2025/08/institutional-digital-asset-derivatives-ecosystem-powering-high-fidelity-rfq-execution-and-optimal-liquidity-discovery.jpg)

## Dealer Behavior

Meaning ▴ In the context of crypto Request for Quote (RFQ) and institutional options trading, Dealer Behavior refers to the aggregate and individual actions, sophisticated strategies, and dynamic responses of market makers and liquidity providers in reaction to incoming trading requests and evolving market conditions. </a><a href="https://prime.greeks.live/area/collusion-detection/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![Interlocking transparent and opaque geometric planes on a dark surface. This abstract form visually articulates the intricate Market Microstructure of Institutional Digital Asset Derivatives, embodying High-Fidelity Execution through advanced RFQ protocols](https://options.greeks.live/wp-content/uploads/2025/07/geometric-abstraction-of-institutional-digital-asset-derivatives-market-microstructure-driving-high-fidelity-execution-via-principal-rfq-protocols-and-optimal-liquidity-aggregation.jpg)

## Collusion Detection

Meaning ▴ Collusion Detection, within crypto trading and decentralized finance, refers to the systematic identification of coordinated, often illicit, activities among multiple market participants aiming to manipulate prices or exploit system vulnerabilities. </a><a href="https://prime.greeks.live/question/to-what-extent-can-machine-learning-techniques-enhance-the-behavioral-realism-of-simulated-market-agents/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![Sleek, intersecting metallic elements above illuminated tracks frame a central oval block. This visualizes institutional digital asset derivatives trading, depicting RFQ protocols for high-fidelity execution, liquidity aggregation, and price discovery within market microstructure, ensuring best execution on a Prime RFQ](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-system-for-optimal-execution.jpg)

## Dealer Agents

Machine learning enhances simulated agents by enabling them to learn and adapt, creating emergent, realistic market behavior. </a><a href="https://prime.greeks.live/question/how-can-an-agent-based-model-improve-the-accuracy-of-simulating-dealer-behavior-in-an-rfq-backtest/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A polished, abstract geometric form represents a dynamic RFQ Protocol for institutional-grade digital asset derivatives. A central liquidity pool is surrounded by opening market segments, revealing an emerging arm displaying high-fidelity execution data](https://options.greeks.live/wp-content/uploads/2025/07/high-fidelity-institutional-digital-asset-derivatives-rfq-protocol-system.jpg)

## Agent Behavior

An agent-based model enhances RFQ backtest accuracy by simulating dynamic dealer reactions and the resulting market impact of a trade. </a><a href="https://prime.greeks.live/area/fair-value/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![The image depicts two intersecting structural beams, symbolizing a robust Prime RFQ framework for institutional digital asset derivatives. These elements represent interconnected liquidity pools and execution pathways, crucial for high-fidelity execution and atomic settlement within market microstructure](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-cross-market-execution-architecture-for-digital-assets.jpg)

## Fair Value

Meaning ▴ Fair value, in financial contexts, denotes the theoretical price at which an asset or liability would be exchanged between knowledgeable, willing parties in an arm's-length transaction, where neither party is under duress. </a><a href="https://prime.greeks.live/area/designated-winner/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![Transparent geometric forms symbolize high-fidelity execution and price discovery across market microstructure. A teal element signifies dynamic liquidity pools for digital asset derivatives](https://options.greeks.live/wp-content/uploads/2025/07/high-fidelity-rfq-execution-for-institutional-digital-asset-derivatives-1.jpg)

## Designated Winner

Meaning ▴ A Designated Winner is the specific party or entity officially selected as successful in a competitive bidding process, auction, or Request for Quote (RFQ) solicitation, based on predetermined evaluation criteria. </a><a href="https://prime.greeks.live/area/rfq-data/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A vertically stacked assembly of diverse metallic and polymer components, resembling a modular lens system, visually represents the layered architecture of institutional digital asset derivatives. Each distinct ring signifies a critical market microstructure element, from RFQ protocol layers to aggregated liquidity pools, ensuring high-fidelity execution and capital efficiency within a Prime RFQ framework](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocols-execution-stack.jpg)

## Rfq Data

Meaning ▴ RFQ Data, or Request for Quote Data, refers to the comprehensive, structured, and often granular information generated throughout the Request for Quote process in financial markets, particularly within crypto trading. </a>

**Tags:** [Algorithmic Collusion](https://options.greeks.live/area/algorithmic-collusion/), [Collusion Detection](https://options.greeks.live/area/collusion-detection/), [Market Simulation](https://options.greeks.live/area/market-simulation/), [Price Discovery](https://options.greeks.live/area/price-discovery/), [Market Microstructure](https://options.greeks.live/area/market-microstructure/), [Agent-Based Model](https://options.greeks.live/area/agent-based-model/)	

## Discover More

- 
### [What Are the Key Differences between Quote Stuffing and Spoofing in Algorithmic Trading?](https://options.greeks.live/question/what-are-the-key-differences-between-quote-stuffing-and-spoofing-in-algorithmic-trading/)
![A precision-engineered component, like an RFQ protocol engine, displays a reflective blade and numerical data. It symbolizes high-fidelity execution within market microstructure, driving price discovery, capital efficiency, and algorithmic trading for institutional Digital Asset Derivatives on a Prime RFQ.](https://options.greeks.live/wp-content/uploads/2025/07/high-fidelity-execution-engine-core-for-institutional-digital-asset-derivatives-rfq-protocol-price-discovery.jpg)

Quote stuffing is an infrastructural attack using message volume to create latency, while spoofing is a psychological tactic using false orders to manipulate perception.

- 
### [How Does the Rfq Process Contribute to Demonstrating Best Execution under Mifid Ii?](https://options.greeks.live/question/how-does-the-rfq-process-contribute-to-demonstrating-best-execution-under-mifid-ii/)
![Transparent geometric forms symbolize high-fidelity execution and price discovery across market microstructure. A teal element signifies dynamic liquidity pools for digital asset derivatives. An opaque dome embodies principal's capital for private quotation and portfolio margin, depicting RFQ protocols on a Prime RFQ.](https://options.greeks.live/wp-content/uploads/2025/07/high-fidelity-rfq-execution-for-institutional-digital-asset-derivatives-1.jpg)

The RFQ process provides a structured, competitive, and auditable data trail for demonstrating best execution under MiFID II.

- 
### [How Does Network Topology Impact RFQ Fill Rates?](https://options.greeks.live/question/how-does-network-topology-impact-rfq-fill-rates/)
![A dark, precision-engineered module with raised circular elements integrates with a smooth beige housing. It signifies high-fidelity execution for institutional RFQ protocols, ensuring robust price discovery and capital efficiency in digital asset derivatives market microstructure.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-rfq-system-core-high-speed-price-discovery-module.jpg)

Network topology governs RFQ fill rates by dictating the speed of price discovery and the mitigation of information leakage risk.

- 
### [Considering Systemic Risk, How Does Flawed Quote Validation Affect Market Stability?](https://options.greeks.live/question/considering-systemic-risk-how-does-flawed-quote-validation-affect-market-stability/)
![Modular, metallic components interconnected by glowing green channels represent a robust Principal's operational framework for institutional digital asset derivatives. This signifies active low-latency data flow, critical for high-fidelity execution and atomic settlement via RFQ protocols across diverse liquidity pools, ensuring optimal price discovery.](https://options.greeks.live/wp-content/uploads/2025/08/institutional-digital-asset-derivatives-rfq-protocol-high-fidelity-execution.jpg)

Flawed quote validation corrodes market stability by distorting price discovery, eroding liquidity, and propagating systemic risk through interconnected financial systems.

- 
### [Can Machine Learning Models Fully Automate the RFQ Process during Extreme Market Stress?](https://options.greeks.live/question/can-machine-learning-models-fully-automate-the-rfq-process-during-extreme-market-stress/)
![A sophisticated metallic mechanism, split into distinct operational segments, represents the core of a Prime RFQ for institutional digital asset derivatives. Its central gears symbolize high-fidelity execution within RFQ protocols, facilitating price discovery and atomic settlement. This system manages fragmented liquidity and optimizes portfolio margin for block trades.](https://options.greeks.live/wp-content/uploads/2025/07/precision-rfq-engine-for-institutional-digital-asset-derivatives-trading.jpg)

ML models enhance RFQ efficiency in stress, yet full automation is precluded by the need for human judgment to manage systemic risk.

- 
### [What Are the Key Differences in Applying TCA to Illiquid versus Liquid Assets in an RFQ?](https://options.greeks.live/question/what-are-the-key-differences-in-applying-tca-to-illiquid-versus-liquid-assets-in-an-rfq/)
![Diagonal composition of sleek metallic infrastructure with a bright green data stream alongside a multi-toned teal geometric block. This visualizes High-Fidelity Execution for Digital Asset Derivatives, facilitating RFQ Price Discovery within deep Liquidity Pools, critical for institutional Block Trades and Multi-Leg Spreads on a Prime RFQ.](https://options.greeks.live/wp-content/uploads/2025/07/high-fidelity-digital-asset-derivatives-rfq-execution-optimization-leveraging-proprietary-institutional-prime-os-for-optimal-price-discovery-and-latency-reduction.jpg)

TCA for liquid assets audits execution price against market data; for illiquid assets, it validates the negotiated price of securing liquidity itself.

- 
### [What Are the Systemic Implications of Widespread Quote Staleness on Market Stability?](https://options.greeks.live/question/what-are-the-systemic-implications-of-widespread-quote-staleness-on-market-stability/)
![A futuristic, metallic structure with reflective surfaces and a central optical mechanism, symbolizing a robust Prime RFQ for institutional digital asset derivatives. It enables high-fidelity execution of RFQ protocols, optimizing price discovery and liquidity aggregation across diverse liquidity pools with minimal slippage.](https://options.greeks.live/wp-content/uploads/2025/07/precision-engineered-platform-for-institutional-digital-asset-derivatives-rfq-protocol-optimized-execution-management-system-with-robust-liquidity-sourcing.jpg)

Systemic quote staleness erodes market stability, necessitating robust informational architectures for superior execution and capital efficiency.

- 
### [How Do Advanced Algorithmic Strategies Integrate with Institutional Crypto Options RFQ Workflows?](https://options.greeks.live/question/how-do-advanced-algorithmic-strategies-integrate-with-institutional-crypto-options-rfq-workflows/)
![A crystalline sphere, representing aggregated price discovery and implied volatility, rests precisely on a secure execution rail. This symbolizes a Principal's high-fidelity execution within a sophisticated digital asset derivatives framework, connecting a prime brokerage gateway to a robust liquidity pipeline, ensuring atomic settlement and minimal slippage for institutional block trades.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-execution-platform-optimizing-liquidity-and-risk-management-through-advanced-protocols.jpg)

Algorithmic strategies enhance institutional crypto options RFQ workflows by optimizing liquidity sourcing, dynamic pricing, and real-time risk management for superior execution.

- 
### [How Do Agent Based Models Differ from Traditional Backtesting Methods?](https://options.greeks.live/question/how-do-agent-based-models-differ-from-traditional-backtesting-methods/)
![Interlocking transparent and opaque geometric planes on a dark surface. This abstract form visually articulates the intricate Market Microstructure of Institutional Digital Asset Derivatives, embodying High-Fidelity Execution through advanced RFQ protocols. It symbolizes Aggregated Inquiry, optimal Liquidity Pool access, and Atomic Settlement within a Principal OS.](https://options.greeks.live/wp-content/uploads/2025/07/geometric-abstraction-of-institutional-digital-asset-derivatives-market-microstructure-driving-high-fidelity-execution-via-principal-rfq-protocols-and-optimal-liquidity-aggregation.jpg)

Agent-based models simulate a market ecosystem to test causality, while traditional backtesting replays historical data to validate correlation.

---

## Raw Schema Data

```json
{
    "@context": "https://schema.org",
    "@type": "BreadcrumbList",
    "itemListElement": [
        {
            "@type": "ListItem",
            "position": 1,
            "name": "Home",
            "item": "https://options.greeks.live"
        },
        {
            "@type": "ListItem",
            "position": 2,
            "name": "Question",
            "item": "https://options.greeks.live/question/"
        },
        {
            "@type": "ListItem",
            "position": 3,
            "name": "Can an Agent-Based Model Be Utilized to Detect Collusive Behavior among Responding Dealers in an Rfq Auction?",
            "item": "https://options.greeks.live/question/can-an-agent-based-model-be-utilized-to-detect-collusive-behavior-among-responding-dealers-in-an-rfq-auction/"
        }
    ]
}
```

```json
{
    "@context": "https://schema.org",
    "@type": "Organization",
    "@id": "https://greeks.live/#organization",
    "name": "Greeks.Live",
    "url": "https://greeks.live/",
    "description": "Greeks.live is the definitive toolkit for the professional options trader, dedicated to empowering our users with institutional-grade data, advanced analytics, and a comprehensive suite of trading tools designed to provide a decisive edge in the market. Every article and guide published under the Greeks.live name is a product of our deep commitment to data-driven analysis and our mission to support the global trading community.",
    "email": "support@greeks.live",
    "logo": {
        "@type": "ImageObject",
        "url": "https://options.greeks.live/wp-content/uploads/2025/07/Greeks.Live-Logo-Icon.svg"
    },
    "hasPart": [
        {
            "@type": "WebSite",
            "name": "About Us",
            "url": "https://about.greeks.live"
        },
        {
            "@type": "WebSite",
            "name": "App Download Page",
            "url": "https://about.greeks.live/#/download/basic_trade"
        },
        {
            "@type": "WebSite",
            "name": "Newcomers' Guide",
            "url": "https://twisty-sign-c8b.notion.site/Guide-for-Greeks-live-X-Deribit-Newbies-5a08a3052822427d800af6eaf3704c8d"
        },
        {
            "@type": "WebSite",
            "name": "Knowledge Base",
            "url": "https://linktr.ee/greeks.live"
        },
        {
            "@type": "WebSite",
            "name": "Option Terminology",
            "url": "https://greekslive.notion.site/88dfc00e7c584d4d8969b6302a79a12f?pvs=4"
        },
        {
            "@type": "WebSite",
            "name": "Terms of Service",
            "url": "https://greeks.live/#/tos"
        },
        {
            "@type": "WebSite",
            "name": "Disclaimer",
            "url": "https://greeks.live/#/disclaimer"
        }
    ],
    "sameAs": [
        "https://x.com/greekslive",
        "https://youtube.com/@greeks_live?si=-KjT0Lkw4WUCI0tD",
        "https://t.me/greekslive_notifications",
        "https://t.me/greekslive",
        "https://pinterest.com/greekslive/"
    ],
    "makesOffer": [
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "RFQ Platform",
                "url": "https://rfq.greeks.live",
                "description": "Execute institutional-grade block trades for crypto options and futures. This platform provides deep, over-the-counter (OTC) liquidity via a professional Request for Quote system.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Lending Tools",
                "url": "https://lend.greeks.live",
                "description": "Manage crypto lending and borrowing activities to earn yield or secure loans. This suite of tools helps users analyze interest rates and manage collateralized debt positions.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Data Lab",
                "url": "https://greeks.live/#/deribit/tools/datalab/BTC",
                "description": "Analyze historical and real-time options data with advanced metrics. This tool is designed for backtesting strategies and identifying market trends through comprehensive data visualization.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Profit & Loss Analysis (PnL)",
                "url": "https://greeks.live/#/deribit/tools/pnl_analysis",
                "description": "Track and analyze the Profit and Loss (PnL) of your options portfolio. This tool provides detailed performance metrics to help traders evaluate and refine their strategies.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Position Visualization",
                "url": "https://greeks.live/#/deribit/tools/pv/BTC",
                "description": "Visualize complex options positions and their corresponding risk profiles. This tool generates payoff diagrams to help traders intuitively understand potential profit, loss, and exposure.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Options Calculator",
                "url": "https://greeks.live/#/deribit/tools/calculator",
                "description": "Calculate the theoretical price and risk parameters ('the Greeks') of crypto options. An essential tool for pricing new trades and analyzing the sensitivity of existing positions.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Greeks.live Main Platform",
                "url": "https://greeks.live/",
                "description": "The central hub for the Greeks.live suite of professional cryptocurrency options and derivatives trading tools. Access analytics, manage positions, and explore the ecosystem.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Community Forum",
                "url": "https://t.greeks.live",
                "description": "Join the official Greeks.live community. Connect with other traders, share insights, and receive the latest updates directly from the team.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Greeks.live for Deribit",
                "url": "http://deribit.greeks.live",
                "description": "A dedicated interface for trading on the Deribit exchange. This platform leverages Greeks.live's advanced analytics and tools specifically for users of Deribit's options and futures markets.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Greeks.live for OKX",
                "url": "http://okx.greeks.live",
                "description": "A dedicated interface for trading on the OKX exchange. This platform integrates Greeks.live's powerful analytics with the OKX marketplace, focusing on its options and derivatives products.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "GLVS.AI Trading Platform",
                "url": "https://www.glvs.ai/",
                "description": "An AI-powered platform for automated crypto options trading. Leverage advanced algorithms and data-driven signals to develop and deploy sophisticated, hands-free trading strategies.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "API Testnet & Sandbox",
                "url": "https://test.greeks.live",
                "description": "A dedicated testnet environment for developers to test their API integrations and automated trading strategies. This sandbox allows for full functionality testing without risking real funds.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        }
    ],
    "subOrganization": [
        {
            "@type": "Organization",
            "name": "GreeksLive HK Co., Limited",
            "email": "support@greeks.live",
            "telephone": "+852-56418949",
            "address": {
                "@type": "PostalAddress",
                "streetAddress": "Unit 11, 27/F, K. Wah Centre, 191 Java Road",
                "addressLocality": "Hong Kong",
                "addressRegion": "North Point",
                "addressCountry": "CN",
                "postalCode": "0000"
            },
            "geo": {
                "@type": "GeoCoordinates",
                "latitude": "22.2932357",
                "longitude": "114.2022026"
            },
            "image": "https://options.greeks.live/wp-content/uploads/2025/08/Wah-Centre-Hong-Kong.jpg"
        }
    ]
}
```

```json
{
    "@context": "https://schema.org",
    "@type": "Article",
    "mainEntityOfPage": {
        "@type": "WebPage",
        "@id": "https://options.greeks.live/question/can-an-agent-based-model-be-utilized-to-detect-collusive-behavior-among-responding-dealers-in-an-rfq-auction/"
    },
    "headline": "Can an Agent-Based Model Be Utilized to Detect Collusive Behavior among Responding Dealers in an Rfq Auction? \u25b4 Question",
    "description": "An Agent-Based Model provides a simulated market ecosystem to detect collusive patterns in RFQ auctions by analyzing behavioral anomalies. \u25b4 Question",
    "url": "https://options.greeks.live/question/can-an-agent-based-model-be-utilized-to-detect-collusive-behavior-among-responding-dealers-in-an-rfq-auction/",
    "datePublished": "2025-08-04T08:19:46+00:00",
    "dateModified": "2025-10-24T20:50:20+00:00",
    "articleSection": [
        "Question"
    ],
    "author": {
        "@id": "https://greeks.live/#organization"
    },
    "publisher": {
        "@id": "https://greeks.live/#organization"
    },
    "image": {
        "@type": "ImageObject",
        "url": "https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-execution-engine-visualizing-market-microstructure-for-high-fidelity-alpha-generation-via-prime-os.jpg",
        "caption": "This abstract visual depicts a sophisticated institutional digital asset derivatives trading ecosystem. Intersecting blue light beams symbolize high-fidelity execution, atomic settlement, and smart order routing for multi-leg spreads. Surrounding mechanical elements represent robust market microstructure, order book dynamics, and aggregated inquiry processing within a Prime RFQ intelligence layer. Large reflective panels signify institutional-grade liquidity pools, facilitating optimal alpha generation and best execution through advanced RFQ protocols."
    },
    "keywords": [
        "Agent-Based Model",
        "Algorithmic Collusion",
        "Collusion Detection",
        "Compliance Technology",
        "Dealer Behavior",
        "Designated Winner",
        "Fair Value",
        "Market Microstructure",
        "Market Simulation",
        "Price Discovery",
        "Quote Dispersion",
        "RFQ Collusion",
        "RFQ Data",
        "RFQ Process",
        "Systemic Risk",
        "Trading Analytics"
    ]
}
```

```json
{
    "@context": "https://schema.org",
    "@type": "WebSite",
    "url": "https://options.greeks.live/",
    "potentialAction": {
        "@type": "SearchAction",
        "target": "https://options.greeks.live/?s=search_term_string",
        "query-input": "required name=search_term_string"
    }
}
```

```json
{
    "@context": "https://schema.org",
    "@type": "FAQPage",
    "mainEntity": [
        {
            "@type": "Question",
            "name": "How Are Dealer Agent Profiles Constructed?",
            "acceptedAnswer": {
                "@type": "Answer",
                "text": "Each dealer agent is parameterized to create a diverse and realistic population of liquidity providers. This prevents simplistic modeling and ensures the simulation captures a range of behaviors."
            }
        }
    ]
}
```


---

**Original URL:** https://options.greeks.live/question/can-an-agent-based-model-be-utilized-to-detect-collusive-behavior-among-responding-dealers-in-an-rfq-auction/
