What Makes Economics “Dismal”?

“The term “dismal science” was coined by the Scottish writer, essayist, and historian Thomas Carlyle in the 19th century to describe the field of economics.” This quote highlights the origin of the term and its derogatory connotations toward economics.
“Carlyle’s critique was aimed at the economists’ belief in the idea that people are fundamentally the same and that institutions like slavery were inherently inefficient and inhumane.” This emphasizes Carlyle’s disagreement with the economic opposition to slavery, underlying the moral and ideological conflict of the era.
“One major challenge in economics is the difficulty of establishing clear cause-and-effect relationships due to the multifaceted nature of economic phenomena.” This highlights the inherent complexities and methodological challenges in economic research.
“The perceived increase in the credibility and predictive accuracy of economic theories and models, along with the period of relative macroeconomic stability experienced in many developed economies in recent decades, can indeed be attributed to several interrelated factors.” This captures the multifaceted advancements in economics that have enhanced its scientific rigor and practical impact on policymaking and economic stability.

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Economics has been called the “dismal science”. Explain the context in which this was said, and comment on the notion.

The term “dismal science” was coined by the Scottish writer, essayist, and historian Thomas Carlyle in the 19th century to describe the field of economics. This derogatory term was used in the context of his 1849 essay, “Occasional Discourse on the Negro Question,” where Carlyle argued against the emancipation of slaves and criticized economists, particularly the English economist John Stuart Mill, for their opposition to slavery on economic grounds. Carlyle’s critique was aimed at the economists’ belief in the idea that people are fundamentally the same and that institutions like slavery were inherently inefficient and inhumane. He lamented the economists’ reliance on statistical analyses and their emphasis on economic systems that prioritize individual freedom and equality, which he saw as a bleak outlook on human capacity and societal potential.

The notion of economics as a “dismal science” has persisted in popular discourse, but its interpretation has evolved over time. Today, it is often invoked to describe the perception that economics focuses on pessimistic predictions or the negative aspects of economic policy and outcomes, such as poverty, inequality, and environmental degradation. However, this view oversimplifies the breadth and depth of the field, which encompasses a wide range of studies on how societies allocate scarce resources to meet the diverse needs and wants of their populations.

Economics, as a discipline, employs rigorous analytical methods to understand complex human behaviors and societal trends. It seeks to offer insights and solutions to some of the most pressing issues facing humanity, including poverty reduction, wealth distribution, resource management, and sustainable development. While it may sometimes deliver “dismal” forecasts or highlight challenging problems, its ultimate aim is to improve societal well-being and inform public policy for the betterment of all.

Thus, while the term “dismal science” captures a historical critique rooted in a specific ideological stance, it does not accurately reflect the comprehensive and dynamic nature of economics as a field of study. Instead, economics provides critical tools and frameworks for understanding and addressing the multifaceted challenges of the modern world.

The term “dismal science” to describe economics was coined by Scottish writer Thomas Carlyle in 1849. Here’s a breakdown of the context and the idea itself:

Context:

Carlyle used the phrase in contrast to the then-popular term “gay science” referring to the art of troubadours.
It’s also believed to be inspired by the gloomy predictions of economist Thomas Malthus, who predicted population growth would always outpace food production, leading to poverty and hardship.

The Notion:

The term is somewhat derogatory, implying economics deals with depressing realities.
There’s some truth to this. Economics often grapples with issues like scarcity, poverty, and inequality.
However, economics also studies ways to improve resource allocation, growth, and overall well-being.
So, while it acknowledges challenges, it also seeks solutions.

In essence, “dismal science” captures a part of economics, but it’s not the whole picture.

Some modern pundits have called economics dismal due to its unwieldy nature in respect to scientific inquiry and measurement. Please provide insight on this notion.

The notion that economics is considered “dismal” due to its perceived unwieldiness in scientific inquiry and measurement stems from the inherent challenges of applying strict scientific methods to study human behavior and societal systems. Unlike the natural sciences, where experimental conditions can often be controlled and variables isolated, economics deals with complex, dynamic systems that are influenced by a multitude of factors, including individual and collective human behaviors, institutional frameworks, cultural norms, and political contexts.

Complexity and Causality: One major challenge in economics is the difficulty of establishing clear cause-and-effect relationships. The multifaceted nature of economic phenomena means that outcomes are often the result of interrelated factors, making it hard to isolate the impact of individual variables. This complexity is compounded by the fact that economic actors (individuals, firms, governments) behave in ways that are influenced by expectations, information asymmetries, and strategic interactions, which can lead to outcomes that are unpredictable or counterintuitive.
Measurement and Data Limitations: Economic research relies heavily on the availability and quality of data. However, accurate data collection can be hampered by limitations in resources, methodologies, and accessibility. Furthermore, the need to make simplifying assumptions to develop economic models can lead to discrepancies between theoretical predictions and real-world observations. The challenge of accurately measuring concepts such as utility, welfare, or the informal economy adds another layer of complexity to economic inquiry.
Methodological Diversity and Disputes: The field of economics encompasses a wide range of methodological approaches, from highly mathematical models to qualitative analyses. This diversity, while a strength, can also lead to debates over the appropriateness and validity of different methods, further complicating the pursuit of definitive conclusions. The existence of competing economic theories and models reflects the difficulty of capturing the full complexity of economic phenomena within a single, unified framework.

Despite these challenges, economics continues to evolve as a discipline, incorporating insights from psychology, sociology, and other fields to develop more nuanced and sophisticated models of human behavior and economic systems. The adoption of advanced statistical techniques, computational methods, and data analytics has also enhanced economists’ ability to analyze complex datasets and test hypotheses.

The criticism that economics is “dismal” because of its methodological challenges overlooks the discipline’s contributions to understanding and addressing key societal issues, such as poverty, inequality, and market failures. While acknowledging its limitations, it is important to recognize that economics provides essential tools for policy analysis and decision-making, aiming to improve societal welfare within the constraints of scarce resources.

You’re right, some modern critics echo the “dismal science” idea by pointing to the limitations of economics as a scientific discipline. Here’s a breakdown of their arguments and some counterpoints:

Criticisms of Economics as a Dismal Science (Modern Version):

Difficulty in Isolating Variables: Unlike physics or chemistry, economic phenomena are influenced by countless factors, making it hard to isolate cause and effect.
Human Behavior: Economic models often rely on assumptions about human behavior, which can be unpredictable and influenced by emotions and social factors.
Historical Specificity: Economic theories may not hold true across different historical periods or cultures.

Counterpoints to the Criticisms:

Progress in Methodology: Economists are constantly developing more sophisticated methods for data analysis and causal inference.
Behavioral Economics: This subfield integrates psychology into economic models to account for human biases and emotions.
Focus on Mechanisms: Modern economics often focuses on understanding the mechanisms that drive economic phenomena, making theories more adaptable.

Overall, while economics may not be a perfect science, it does strive to be rigorous and evidence-based. The field acknowledges its limitations and constantly refines its methods to provide better insights into economic behavior and policy.

Here are some additional points to consider:

Predictive Power: While economics may not perfectly predict future events, it can identify trends and potential risks, informing better decision-making.
Policy Formulation: Economic models, though imperfect, are valuable tools for policymakers to evaluate potential outcomes of different interventions.

So, the debate about economics as a “dismal science” continues. However, recognizing its limitations shouldn’t diminish the valuable role economics plays in understanding our complex world.

The perceived credibility and predictive accuracy of economic theories and models appear to have increased substantially in recent decades, accompanied by a period of relative macroeconomic stability in many developed economies. To what extent can this phenomenon be attributed to the proliferation of empirical economic research and controlled experiments, advances in theoretical modeling and econometric techniques, the use of technological innovations in data collection and computational analysis, or other potential factors? And how has the interplay between these elements contributed to the discipline’s evolving scientific rigor and its ability to inform policymaking and anticipate economic fluctuations?

The perceived increase in the credibility and predictive accuracy of economic theories and models, along with the period of relative macroeconomic stability experienced in many developed economies in recent decades, can indeed be attributed to several interrelated factors. These include the proliferation of empirical economic research and controlled experiments, advancements in theoretical modeling and econometric techniques, the incorporation of technological innovations in data collection and computational analysis, among others. The interplay between these elements has significantly contributed to the discipline’s evolving scientific rigor and its capacity to inform policymaking and anticipate economic fluctuations.

Proliferation of Empirical Economic Research and Controlled Experiments: The rise of empirical economic research, particularly the use of controlled experiments and natural experiments, has allowed economists to test theories and models with real-world data, enhancing the empirical basis of economic science. This approach has been especially influential in fields such as development economics, where randomized controlled trials (RCTs) have been used to measure the effects of policy interventions on poverty, education, and health outcomes. These methods have increased confidence in the predictive power of economic models by providing robust evidence on causal relationships.
Advancements in Theoretical Modeling and Econometric Techniques: Theoretical developments have expanded the range and depth of economic models, allowing for more nuanced analysis of complex phenomena. Concurrently, advances in econometric techniques have improved the ability of economists to analyze data, control for confounding variables, and make more accurate inferences about causal relationships. These improvements have bolstered the discipline’s capacity to develop models that better reflect the complexity of real-world economic interactions and outcomes.
Incorporation of Technological Innovations in Data Collection and Computational Analysis: Technological advancements have revolutionized data collection and computational analysis, enabling economists to handle large datasets (“big data”) and perform sophisticated simulations and econometric analyses that were previously infeasible. Machine learning and artificial intelligence have also been integrated into economic research, offering new tools for prediction and analysis. This technological leap has greatly enhanced the precision and depth of economic analysis, facilitating more accurate forecasts and policy evaluations.
Interdisciplinary Approaches and Behavioral Economics: The integration of insights from psychology, sociology, and other disciplines into economics, particularly through the field of behavioral economics, has enriched economic models with a more realistic understanding of human behavior. This cross-pollination has improved the predictive accuracy of economic theories by accounting for non-rational decision-making processes and other real-world complexities.
Policy Development and Economic Stability: The advancements in economic science have had tangible impacts on policy development, contributing to periods of macroeconomic stability. Better theoretical and empirical understanding of economic mechanisms has enabled policymakers to devise more effective monetary, fiscal, and regulatory policies. These policies have helped mitigate the severity of economic fluctuations and contributed to sustained growth and stability in many developed economies.

The synergy between these factors has significantly enhanced the scientific rigor of economics. By improving the discipline’s methodological foundations and expanding its empirical evidence base, economists have made substantial progress in understanding the complex dynamics of economies. This evolution has not only advanced the academic discipline but also improved the capacity of economics to inform effective and responsive policymaking, thereby playing a crucial role in promoting economic stability and addressing societal challenges.

You’ve identified some key factors that have likely contributed to the perception of increased credibility and predictive accuracy in economic theories and models. Here’s a breakdown of their potential influence and how they interact:

Factors Contributing to Perceived Credibility and Predictive Accuracy:

Empirical Research and Controlled Experiments:
- The rise of empirical studies using real-world data has helped ground economic theories in evidence.
- While controlled experiments in economics are challenging due to ethical and practical reasons, quasi-experimental methods have allowed for more rigorous causality testing.
Advancements in Theoretical Modeling and Econometric Techniques:
- More sophisticated economic models that better capture real-world complexities have been developed.
- Improved econometric techniques allow for stronger statistical analysis and identification of causal relationships within economic data.
Technological Innovations in Data Collection and Analysis:
- The explosion of digital data and advancements in data collection methods (e.g., web scraping, sensor networks) have provided economists with a richer and more detailed picture of the economy.
- Increased computational power allows for processing of massive datasets and the application of complex statistical models.

Interplay and Impact:

These factors work together to strengthen the scientific rigor of economics.
Empirical research informs model development, which is then tested using advanced econometrics.
Data analysis with new technologies allows for continuous refinement and validation of theories.
This improved scientific foundation enhances the ability of economic models to:
- Inform Policymaking:
  - By analyzing potential outcomes of policy interventions, economists can provide more robust recommendations for policymakers.
- Anticipate Economic Fluctuations:
  - More accurate models can identify trends and potential risks, allowing for proactive measures to address them.

However, it’s important to consider limitations:

Economic models are still simplifications of reality, and human behavior remains a challenge to fully account for.
The field is constantly evolving, and past successes don’t guarantee future accuracy.

Overall, the interplay between empirical research, theoretical advancements, technology, and econometrics has likely contributed to a perception of greater credibility and predictive power in economics. This, in turn, has enhanced the field’s ability to inform policy and anticipate economic fluctuations.

Other Potential Factors:

Increased Focus on Specific Phenomena: Research may have become more targeted, leading to deeper understanding of specific economic issues.
Greater Transparency and Open Access to Data: This allows for independent verification of research findings and fosters collaboration.

The field of economics is constantly evolving, and the debate about its effectiveness will likely continue. However, the trends you’ve identified point towards a discipline striving for greater scientific rigor and practical application.

How has the evolution of economics affected the evolution of economics programs in universities? How has the recommended skill set changed over the decades?

The evolution of economics as a discipline has had a profound impact on the structure and content of economics programs in universities. These changes reflect the broadening scope of the field, methodological advancements, and the increasing importance of data analysis in economic research and policymaking. As the discipline has grown in complexity and sophistication, the recommended skill set for economics students has also evolved significantly over the decades.

Early to Mid-20th Century

Focus on Theory and History: Initially, economics programs were heavily focused on economic theory and the history of economic thought. Students were expected to have a strong foundation in the principles of economics and an understanding of the evolution of economic ideas and policies.

Late 20th Century

Quantitative and Analytical Skills: As the field began to emphasize more quantitative methods, economics programs started incorporating more mathematics and statistics into their curricula. This shift was in response to the growing use of mathematical models and statistical analysis in economic research. Skills in calculus, linear algebra, and statistical theory became crucial for understanding and conducting economic analysis.
Computational Skills: With the advent of computers and software, computational skills started to become more important. Students were encouraged to learn programming languages and software tools for data analysis, such as Stata, SAS, or R, to perform econometric analyses and manipulate large datasets.

Early 21st Century to Present

Data Science and Technological Proficiency: The explosion of big data and the advancement of computational technologies have further transformed economics programs. Knowledge of data science, machine learning, and advanced econometrics is increasingly seen as essential. Students are now often required to be proficient in more complex programming languages (e.g., Python, R) and tools for data analysis and visualization.
Interdisciplinary Approach: Economics programs have also become more interdisciplinary, reflecting the field’s integration of insights from psychology (behavioral economics), political science (political economy), and environmental science (environmental economics), among others. This broadened perspective necessitates a diverse skill set, including the ability to engage with literature and methodologies from related fields.
Soft Skills and Critical Thinking: Beyond technical skills, there’s an increased emphasis on developing soft skills such as critical thinking, problem-solving, effective communication, and the ability to work in diverse teams. These skills are vital for applying economic reasoning and quantitative analysis to real-world problems and policy questions.

Looking Forward

Ethics and Global Perspective: With growing awareness of global challenges such as inequality, climate change, and international development, economics programs are increasingly incorporating courses on ethics and global economic issues. Understanding the ethical implications of economic decisions and policies, as well as having a global perspective, are becoming key components of the skill set for new economists.

In summary, the evolution of economics has led to a corresponding evolution in economics programs at universities, with a significant shift towards quantitative and computational skills, interdisciplinary approaches, and a broader understanding of global and ethical issues. This change reflects the discipline’s ongoing efforts to remain relevant and effective in addressing complex economic problems in a rapidly changing world.

The evolution of economics as a field has definitely impacted how economics programs are structured in universities. Here’s a look at the cause-and-effect relationship:

Changes in Economics, Changes in Curriculum:

Shift from “Dismal Science” to Empirical Focus: As economics moved away from pure theory and embraced empirical research, economics programs incorporated more data analysis, statistics, and econometrics courses.
Rise of Subfields: The emergence of new areas like behavioral economics, development economics, and environmental economics led to the introduction of specialized courses and even entire tracks within economics programs.
Focus on Technology and Problem-Solving: The increasing role of technology in data collection and analysis is reflected in programs offering courses on big data, coding, and computational methods. Additionally, there’s a growing emphasis on applying economic theory to solve real-world problems.

Impact on Skill Sets:

Quantitative Skills: The ability to handle complex data, use statistical software, and conduct econometric analysis is now a core skill set for economists.
Computer Programming: Coding skills are becoming increasingly valuable for economists to manipulate and analyze data.
Communication and Storytelling: Economists need to effectively communicate complex concepts and findings to a variety of audiences, both technical and non-technical.

Beyond the Core:

Interdisciplinary Approaches: Economics programs are recognizing the value of interdisciplinary approaches, encouraging students to take courses in fields like psychology, sociology, and computer science to complement their economics foundation.
Soft Skills: Critical thinking, problem-solving, and the ability to work effectively in teams are essential skills for economists in any field.

Not all change is uniform:

The specific curriculum changes may vary depending on the university’s focus (e.g., research-oriented vs. undergraduate-focused) and program type (e.g., Bachelors, Masters, PhD).
The pace of change may also differ – some programs adapt rapidly, while others may take longer to incorporate new trends.

Overall, the evolution of economics has led to economics programs becoming more quantitative, data-driven, and interdisciplinary, while equipping students with a broader skill set that goes beyond traditional economic theory.

Create a 10-item quiz on the discussion above with answers at the end.

Quiz: Evolution of Economics and Its Impact on University Programs

Questions

What was the primary focus of early economics programs in universities?

A) Data analysis
B) Economic theory and the history of economic thought
C) Machine learning techniques
D) Environmental economics

Which skills became crucial for understanding and conducting economic analysis by the late 20th century?

A) Communication skills
B) Quantitative and analytical skills
C) Ethical decision-making
D) Global economic policies

What shift in skillset did the advent of computers and software bring about in economics programs?

A) Emphasis on learning ancient languages
B) Increased importance of computational skills
C) Focus on manual data collection
D) Priority to physical education

By the early 21st century, which programming languages are students often required to be proficient in for data analysis in economics?

A) HTML and CSS
B) Python and R
C) Cobol and Fortran
D) Swift and Kotlin

What has contributed to economics programs becoming more interdisciplinary?

A) The integration of insights from fields such as psychology and political science
B) The decreasing importance of mathematics
C) A shift away from quantitative methods
D) Reduced interest in global economic issues

Which of the following is not listed as a skill increasingly emphasized in modern economics programs?

A) Critical thinking
B) Problem-solving
C) Effective communication
D) Calligraphy

How has the advent of big data affected economics programs at universities?

A) Decreased focus on data science
B) Reduced emphasis on technological proficiency
C) Increased emphasis on data science and technological proficiency
D) Focus shifted entirely to historical analysis

Which interdisciplinary approach is mentioned as being part of the evolving curriculum in economics programs?

A) Behavioral economics
B) Astrology
C) Mythology studies
D) Paleontology

What new components are becoming key in the skill set for new economists, reflecting global challenges?

A) Ethics and global perspective
B) Localized economic models only
C) Ignoring technological advancements
D) Focusing solely on economic theory

Which of the following best describes the trend in economics education from the early to the 21st century?
- A) Moving from a focus on theory to a more applied, data-driven, and interdisciplinary approach
- B) Shifting away from quantitative methods to exclusively qualitative analyses
- C) Decreasing emphasis on computational and analytical skills
- D) Narrowing the curriculum to focus solely on traditional economic models

Answers

B) Economic theory and the history of economic thought
B) Quantitative and analytical skills
B) Increased importance of computational skills
B) Python and R
A) The integration of insights from fields such as psychology and political science
D) Calligraphy
C) Increased emphasis on data science and technological proficiency
A) Behavioral economics
A) Ethics and global perspective
A) Moving from a focus on theory to a more applied, data-driven, and interdisciplinary approach

Quiz: The Evolving Science of Economics

Instructions: Choose the best answer for each question.

The term “dismal science” to describe economics was coined by whom? a) Thomas Carlyle
b) Adam Smith c) John Maynard Keynes d) Milton Friedman
What is a criticism of economics as a “dismal science” (modern version)? a) It focuses on finding solutions to economic problems. b) It struggles to isolate specific variables for study. c) It relies too heavily on historical data. d) It ignores the role of technology in economic growth.
Which of the following is NOT listed as a potential factor contributing to the perceived increase in economic theories’ credibility? a) Increased focus on theoretical modeling b) Technological advancements in data collection c) Greater emphasis on ethical considerations d) Development of more sophisticated econometric techniques
How does empirical research influence economic models? a) It provides real-world data to test and refine models.
b) It focuses solely on developing complex mathematical formulas. c) It discourages the use of new and innovative theories. d) It prioritizes philosophical debates over practical applications.
What is a potential limitation of economic models, even with advancements? a) They perfectly predict future economic events. b) They may not fully account for unpredictable human behavior.
c) They always rely on outdated historical data. d) They are not relevant to informing policy decisions.
How has the concept of “dismal science” impacted the development of economics programs? a) It led to a focus on memorizing economic theories. b) It encouraged a shift towards empirical research methods. c) It discouraged the use of technology in economic analysis. d) It emphasized the negative aspects of economic phenomena.
Which skill set is NOT considered important for modern economists? a) Public speaking and clear communication b) The ability to code and use statistical software c) Extensive knowledge of historical economic events d) Critical thinking and problem-solving skills
Why might economics programs incorporate courses from other disciplines? a) To provide students with a well-rounded understanding of economic issues. b) To replace core economics courses with non-relevant subjects. c) To reduce the overall workload for economics majors. d) To discourage students from pursuing careers in economics.
The interplay between empirical research, theoretical advancements, technology, and econometrics likely contributes to: a) A decrease in the perceived value of economic models. b) A decline in the field’s ability to inform policymaking. c) A perception of greater credibility in economic theories. d) A rise in the number of economic crises.
How might the curriculum of an economics program differ depending on the university? a) All programs offer a standardized curriculum regardless of university. b) Research-oriented universities may offer more advanced courses. c) Undergraduate programs may focus on foundational economic principles. d) All of the above

Answers:

a) Thomas Carlyle
b) It struggles to isolate specific variables for study.
c) Greater emphasis on ethical considerations
a) It provides real-world data to test and refine models.
b) They may not fully account for unpredictable human behavior.
b) It encouraged a shift towards empirical research methods.
c) Extensive knowledge of historical economic events
a) To provide students with a well-rounded understanding of economic issues.
c) A perception of greater credibility in economic theories.
d) All of the above

Provide 15 discussion questions relevant to the content above.

How has the emphasis on quantitative methods in economics influenced the type of research questions economists ask today compared to several decades ago?
In what ways do modern computational tools and data analysis techniques improve economic forecasts and policy recommendations?
Discuss the potential benefits and challenges of incorporating interdisciplinary approaches, such as insights from psychology and political science, into economic research and education.
How do randomized controlled trials (RCTs) change the landscape of development economics, and what ethical considerations arise from their use?
Examine the role of ethics in economics education, especially in the context of global economic challenges like inequality and climate change.
How has the integration of machine learning and artificial intelligence into economic studies altered the skills required of economics graduates?
Reflect on the impact of big data on the field of economics. Does it always lead to better outcomes, or are there potential pitfalls in relying heavily on large datasets?
Consider the implications of a more data-driven approach to economics on policy-making. Can data alone solve complex economic problems, or is there still a need for theory?
How does the shift towards a more applied and interdisciplinary economics curriculum prepare students for the current job market and future economic challenges?
Discuss the importance of soft skills, such as critical thinking and effective communication, in the field of economics. How do these skills complement technical knowledge in professional settings?
In what ways have advancements in econometric techniques expanded the possibilities for causal inference in economics research?
How can economics programs address the challenge of teaching students to apply economic reasoning to real-world problems effectively?
Explore the significance of behavioral economics in modern economic theory and practice. How does understanding human behavior change the way economists approach policy design?
Discuss the evolution of mathematical models in economics. How have these models adapted to better reflect the complexity of real-world economic interactions?
How does the global perspective in economics education help students understand and address international economic issues?

Discussion Questions: The Evolving Science of Economics

Do you think the term “dismal science” is a fair characterization of economics? Why or why not?
How can economic models be improved to better account for human behavior?
What are some ethical considerations that economists should take into account when conducting research or developing models?
In your opinion, what is the most valuable skill for an economist to possess in today’s world? Why?
Discuss the potential benefits and drawbacks of incorporating technology like big data and coding into economics programs.
Should economics programs prioritize theoretical foundations or practical applications of economic principles? Why?
How can economists bridge the gap between complex economic models and clear communication for the public?
Do you think advancements in economics have made a significant impact on real-world policy decisions? Provide examples if possible.
What are some potential limitations of using historical economic data to predict future trends?
Imagine you are designing a new economics course. What interdisciplinary elements from other fields would you incorporate, and why?
How can economic models be adapted to account for global economic issues and interdependencies between countries?
In what ways might the increasing focus on data privacy impact the collection and analysis of economic data?
Discuss the potential role of behavioral economics in understanding and influencing consumer behavior.
How can economic theories be applied to address issues like climate change or income inequality?
With the growing importance of technology in the economy, what new subfields of economics might emerge in the future?

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