# Modeling and Resolving Uncertainty in DIKWP Model
## 1 Introduction  
In the era of digital finance, the emergence of high-frequency trading (HFT) systems signifies a significant advancement in financial markets, utilizing sophisticated algorithms and high-speed data networks to execute trades at speeds measured in milliseconds or even microseconds, surpassing the capabilities of human traders [1]. These systems analyze market conditions and execute orders based on predetermined criteria, aiming to profit from minute price discrepancies across different trading venues. The introduction of HFT is marked by its ability to enhance liquidity, narrow spreads, and improve market efficiency, yet it has also raised concerns regarding market stability and fairness [2]. Within these HFT systems, proprietary algorithms are strictly confidential, making it challenging for regulatory agencies and participants to fully comprehend their operations and impact [3, 4]. The Dodd-Frank Wall Street Reform and Consumer Protection Act, enacted in response to the 2008-2009 financial crisis, significantly reshaped the of the U.S. financial industry. Aimed at preventing the recurrence of such a devastating economic downturn, the Act introduced comprehensive reforms designed to increase transparency, strengthen financial stability, and enhance consumer protection across financial markets [5]. Notably, the legislation impacted areas including HFT, which has become a prevalent practice due to technological advancements in trading platforms. The Dodd-Frank Act addresses concerns related to HFT by implementing stricter oversight and reporting requirements for trades and entities engaged in this practice. This includes increased scrutiny on liquidity provision, manipulative trading practices, and the systemic risk posed by the high-speed, algorithm-driven trading strategies that characterize HFT [6]. These measures reflect a broader regulatory effort under Dodd-Frank to ensure a more resilient financial system bymandating a range of risk management protocols and enhancing the overall accountability of financial institutions. Against this backdrop, HFT enterprises confront unprecedented compliance challenges, particularly due to the ambiguous definition of HFT and regulatory boundaries within the legislation. These challenges not only affect the daily operations of enterprises but also significantly impact their strategic decision-making. To address these challenges, we introduce the Data, Information, Knowledge, Wisdom, and Purpose (DIKWP) model, which originated from Professor [PERSON]'s research on artificial consciousness and artificial intelligence [7; 8], aiming to conduct an in-depth analysis of the key issues faced by HFT in the regulatory compliance process through this model. The DIKWP model provides a systematic analytical framework by distinguishing and correlating five dimensions: Data, Information, Knowledge, Wisdom, and Purpose, and processing them into corresponding Data Graphs (DGs), Information Graphs (IGs), Knowledge Graphs (KGs), Wisdom Graphs (WGs), and Purpose Graphs (PGs). Under the guidance of purpose [9], it assists financial enterprises in better understanding and managing complex regulatory requirements. Specifically, this study categorizes challenges as the \"4-N\" problems, Incompleteness, Inconsistency, Imprecision, and Incorrectness, which highlight the uncertainties faced by enterprises in complying with regulations. To address the challenges of uncertainty, we will explore and analyze the action strategies and decision-making processes of HFT enterprises in the face of the Dodd-Frank Act through Cognitive Space, Concept Space, and Semantic Space:  
* Cognitive Space (ConN) focuses on cognitive activities in the decision-making process, including how to identify, process, and utilize information to form knowledge, wisdom, and purpose, to support compliance and business decisions;
* Concept Space (ConC) provides us with a framework for understanding and organizing the relationship between regulatory requirements and business practices.