Practical No. 2: Essay on "Current Developments and Future of AI in Economics".

Current Developments and Future of AI in Economics

– Julius C. Ujah

1.0 Introduction:

In recent years, the pervasive presence of artificial intelligence (AI) has captured global attention, suggesting that its emergence is the hallmark of our era (Makridakis, 2017). Indeed, AI technologies manifest in diverse forms, permeating all facets of societal interaction, from everyday encounters with chatbots to the sophisticated systems facilitating industrial and governmental operations, quietly reshaping lifestyles worldwide (Li, Hou, Yu, Lu & Yang, 2017).

Conventionally, AI is delineated as a branch of computer science dedicated to crafting data processing systems capable of emulating human cognitive functions, such as learning, reasoning, and self-enhancement (Peres, Jia, Lee, Sun, Colombo & Barata, 2020). Trifan and Buzatu (2020) defined AI as machine learning, wherein neural networks are trained on datasets, with resource allocation, data availability, and computational prowess constituting its foundational pillars.

Unlike preceding technologies, AI boasts a unique capacity to refine its proficiency in specific tasks over time, owing to its innate learning capabilities. Its overarching objective is to aid humans in optimal decision-making processes. Consequently, AI integration into operating systems aims to forge systems capable of augmenting human decision-making or even assuming complete autonomy in decision processes (Gomes, da Silva, Pinto, Centoamore, Digiesi, Facchini, Neto, 2020).

Gradually, AI evolves into an indispensable technological enabler of daily social existence and economic endeavors (Naimi-Sadigh et al., 2021). Its substantial contributions to sustainable economic growth across diverse sectors are increasingly apparent, propelling it into the spotlight of industry, academia, and governmental spheres (Heylighen, 2017).

Specifically, as Artificial Intelligence (AI) is revolutionizing various fields, its impact on economics is no exception Makridakis (2017). In recent years, the integration of AI in economic processes has grown significantly, reshaping the way we analyze, predict, and make decisions. This essay explores the current developments and anticipates the future trajectory of AI in the field of economics.

2.0 Current Developments:

AI has been instrumental in enhancing data analysis and decision-making in economics. Machine learning algorithms, a subset of AI, have demonstrated remarkable capabilities in handling vast datasets and extracting valuable insights (Xu, Liu, Cao, Huang, Liu, Quian & Zhang, 2021). Predictive modeling, for instance, has become more accurate with AI algorithms identifying complex patterns that human analysts might overlook.

Moreover, AI-powered tools have transformed financial markets. High-frequency trading, driven by AI algorithms, reacts to market changes at unprecedented speeds, influencing pricing dynamics and liquidity. Automated trading systems leverage AI to execute trades efficiently, minimizing risks and maximizing returns (OECD, 2021)

In economic research, AI is streamlining data collection and analysis. Natural Language Processing (NLP) algorithms can process vast amounts of text, facilitating the extraction of relevant information from academic papers, reports, and news articles (Crowston, Allen, Heckman, 2012). This expedites the research process and enables economists to stay updated on the latest developments.

• See  attached Fig 1

3.0 Future Developments:

Looking ahead, the role of AI in economics is poised to expand further. One key area of growth is in policy making. Governments and international organisations are increasingly relying on AI models to simulate the potential impacts of various economic policies. These simulations provide valuable insights into the potential consequences of different decisions, aiding policymakers in making informed choices (Wirjo, Calizo, Vasquez, San Andres, 2022).

AI is also expected to play a pivotal role in addressing economic inequality. By analyzing socio-economic data, machine learning algorithms can identify patterns and propose targeted interventions to address disparities. This has the potential to create more inclusive economic policies that benefit a broader spectrum of society (Qin, Xu, Wang, Skare, 2023).

Furthermore, AI-powered personal assistants are likely to become ubiquitous in economic activities. Virtual financial advisors, leveraging AI algorithms, can provide personalized investment advice, budgeting tips, and financial planning, democratizing access to financial expertise (OECD, 2021).

• See attached Fig 2

4.0 Conclusion:

The integration of AI in economics is an ongoing journey, marked by current advancements and promising prospects. As AI continues to evolve, it will reshape economic research, financial markets, policy-making, and personal financial management. Embracing these technological advancements responsibly and ethically will be crucial in unlocking the full potential of AI in shaping a more efficient, equitable, and resilient economic landscape.

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