# SUMMARY - The World in the Model - Morgan, Mary S_

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Here is a summary of the key points about how economic modeling developed as an epistemic genre in economics according to the passage:

Modeling emerged as a distinct style of reasoning in economics in the late 19th century, gradually becoming the dominant mode of inquiry.

Early modelers like Walras, Jevons and Marshall experimented with mathematical and graphical representations to formalize economic concepts and relationships.

Specific historical models like Quesnay's tableau economique and Ricardo's farm model illustrations provide insights into how early economists began developing rules and representations.

Modeling involves both giving form to ideas through analogies, equations, diagrams, etc. and subjecting those representations to formal rules of manipulation and deduction.

As a genre, modeling shaped how economists think about and investigate the economy over time. To understand its development requires analyzing both models themselves and modeling as a cognitive process.

The gradual acceptance and naturalization of modeling changed the content and methodology of economics, making formal tools and objects of analysis central to the discipline.

So in summary, the passage examines modeling as both a historically evolving epistemic practice in economics as well as a cognitive process involving imagination, formalization and rule-based reasoning. Understanding its development requires attention to specific models and modeling as a distinctive style of scientific inquiry.

Here is a summary:

Ricardo used numerical examples and simple models in his economic writings to illustrate and demonstrate economic principles, even though his writing style itself was verbose and difficult.

His conceptualization of "models" differed from the modern understanding - for classical economists like Ricardo, the economy was governed by strict natural laws, not simplified representations or models as understood today.

A key example Ricardo used was adding incremental teams of laborers (10 workers at a time) to cultivate a field. This allowed him to demonstrate the operation of his laws of distribution as the size of the workforce varied.

By integrating different numerical examples and parameters into a single conceptual framework, Ricardo built up simple models to explain economic phenomena and relationships in a way that was novel for his time.

While rudimentary by modern standards, Ricardo's use of numerical examples and basic model-building laid the groundwork for the development of economic modeling as a tool for reasoning, demonstration, and policy analysis. He helped pioneer this methodology in classical political economy.

Here is a summary of the key points:

David Ricardo used numerical examples and accounting models of a hypothetical "model farm" to demonstrate economic laws and concepts.

His model farms integrated various factors like rent, profits, wages, land quality, technology, and capital investments into conceptual scenarios.

This allowed him to reason through how changes in one area would impact distributions of wealth between landlords, capitalists, and laborers.

The model farm examples mirrored real experimental farming reports of the time, which documented outputs, costs, and profits under different conditions.

Conducting numerical experiments on his model farm helped Ricardo identify unexpected insights and derive more complex implications of economic changes over time.

The process of developing and refining these integrated models was an important part of Ricardo's methodology for exploring ideas and formulating economic theories.

His work drew on empirical agricultural knowledge but applied quantitative analysis to build generalized principles of how economic systems functioned.

So in summary, Ricardo used model farms and numerical accounting as a tool for theoretical discovery and demonstration, integrating real-world data in a way that paralleled experimental methodology in agriculture. This blended empirics with theory formation.

Here is a summary of the key points about Ricardo's use of modeling in classical economics:

Ricardo used numerical modeling through his "model farm" accounts to systematically test relationships between economic variables like rents, profits and wages under different scenarios.

The model farm represented both individual farms and the agricultural sector as a whole, allowing him to demonstrate how changes in one variable impacted others across different scales.

By experimenting over successive periods rather than just static alternatives, Ricardo was able to observe emerging patterns like the potential for falling profit rates with capital accumulation over time.

Though aimed at illustrating theory rather than empirical accuracy, the model still drew on contemporary farming data. It generated valid theoretical insights even if numbers were not real-world precise.

Accounting conventions integrated the model's variables so they behaved consistently according to Ricardo's theoretical “laws of distribution.” Changes in one part rippled through the whole model.

The model farm approach allowed Ricardo to systematically deduce general economic principles from numerical experiments, overcoming limitations of verbal/tabular reasoning alone. It helped formalize his classical system.

So in summary, Ricardo's use of modeling represented an early empirical application of using numerical techniques to explore economic theory in a more rigorous, integrated manner.

Here is a summary of the key points about the development of the Edgeworth Box model:

The Edgeworth Box originated from a 1881 diagram by Francis Ysidro Edgeworth depicting the exchange of two goods between two individuals. It was not actually a box shape at first.

Vilfredo Pareto in 1906 was the first to orient the individuals at opposing corners of a rectangle, giving rise to the standardized box shape representation.

Later contributors like Lenoir and Bowley placed the initial endowment point inside the box rather than each individual starting with their full endowment outside, allowing indifference curves to be drawn fully inside.

This influenced economics to focus on efficiency within given endowments rather than addressing distributional issues.

Details of how the box was drawn, like whether axes met or extended beyond it, carried meaning for different analysts seeking to represent economic concepts.

Over time the model became more standardized and complex as it was applied to more scenarios, losing some of the early flexibility but gaining representational power.

The box effectively represents resource constraints through its fixed or variable size under different economic conditions.

So in summary, it traces the evolution of the box from its origins to its modern form and usage, highlighting key conceptual developments along the way.

Here are the key points summarized:

Early economists like Adam Smith provided more nuanced descriptions of human behavior rather than simplified models, which were too complex for analysis.

Thomas Malthus was an early pioneer of using simplified models of "economic man" to analyze behaviors and economic laws. His model focused on self-interest and reproduction.

Later portrayals like Mill's stripped economic man down further to just motivations relevant to political economy, like desire for wealth. This allowed for more scientific analysis.

Concepts like Weber's ideal types and Menger's human economy moved to even more abstract conceptualizations as analytic tools, not empirical descriptions.

Menger and Jevons developed conceptual portraits of economic behavior through concept formation rather than empirical testing. Jevons mathematized the model, allowing further development and new applications.

Jevons' model conceptualized economic man as seeking to maximize utility through consumption choices, representing behaviors mathematically. This influenced later models and tied economics more to formalism.

So in summary, the models and portrayals of economic man evolved from richer descriptions to increasingly abstract and idealized conceptualizations to serve as analytic tools for understanding behaviors and developing economics as a scientific discipline.

Here is a summary of the key points about the Newlyn-Phillips Machine:

It was constructed in 1949-1950 at the National Bureau of Economic Research to serve as a physical model of the macroeconomy.

The model used colored water flowing through a system of interconnected pipes, tanks, pumps, and valves to represent different economic variables like consumption, investment, income, imports, exports, etc.

The levels and flows of water were directly analogous to levels and changes in key macroeconomic aggregates like GDP, consumption, investment, etc.

It allowed researchers to physically manipulate variables like government spending, taxes, interest rates, etc. and observe the dynamic reactions in other connected variables via changes in water flows and levels.

This tangible, hands-on modeling approach gave insights not possible through purely theoretical or mathematical models at the time. Researchers could test hypotheses by direct experimentation.

It was an early example of using a purpose-built physical system to model the relationships and interdependencies in a complex real-world system like the national economy.

While a simplified portrayal, it showed how hydraulic analogies could represent economic concepts and interactions in a way that was both intuitively graspable and experimentally manipulable.

So in summary, it was an innovative early hydraulic/physical model of the macroeconomy that allowed dynamic experimentation with economic policies.

Here is a summary of the key concepts discussed in the passage:

National income - The total value of all final goods and services produced within a country in a given period of time (usually a year).

Expenditure - The total amount spent on final goods and services within an economy over a period of time. The main components are consumption, investment, government spending, and net exports.

Consumption - Spending by households on final goods and services for personal use. This includes both durable and non-durable goods. Consumption is the largest component of expenditure.

Savings - The portion of income that is not spent on consumption or taxes. Savings provide funds for investment.

Investment - Spending on capital goods like buildings, machinery, technology, etc. that will be used in the production process. Investment leads to expansion of the productive capacity of the economy.

The concepts are interrelated and represent the circular flow of income and expenditure in a national economy. The Mark I and Mark II machines were early attempts at demonstrating these relationships dynamically through a physical hydraulic model. This helped teach and illustrate macroeconomic theories and policies visually.

Here is a summary of the key points:

Ragnar Frisch developed one of the early mathematical models of business cycles in the 1920s-1930s to endogenously generate cyclical patterns in economic activity over time.

His initial model could produce cycles, but they were too neatly periodic compared to real data. He augmented the model by introducing random shocks that propagated over time, producing irregular cycle patterns matching real world data.

Frisch drew on analogies like a randomly struck rocking horse to shape how random impulses were incorporated. Schumpeter's theory of innovation-driven cycles also informed the model.

Younger economists like Meade and Samuelson also developed simple algebraic/geometric models to represent Keynes' words and capture the essence of his ideas, allowing comparison to classical theory. Meade established equilibrium conditions and checked his model's stability and response to certain changes.

These early 20th century business cycle and Keynesian models employed both visual diagrams and increasing mathematical formalization to represent economic dynamics and hypotheses in a way that could generate testable implications.

Here is a summary of the key points:

Economic models are flexible and regularly adapted as new theories, evidence, and problems emerge. What counts as plausible changes over time within the modeling genre used by economists.

Plausibility judgments are made based on how well a model's narratives/stories fit accepted scientific knowledge - both conceptual understanding and empirical evidence. Models are tested against this broader context.

Narratives allow models to "explain" real-world phenomena, but plausibility depends on the looseness of fit between model and world, not proof of truth. Models offer parables rather than fables about immovable laws.

By generating and critiquing narratives, economists can explore model implications and create coherent accounts of events, exploring the abstract and concrete levels of analysis that models bridge. This informs judgments of meaningfulness, credibility, and where models may be lacking.

Modeling forms the epistemic genre that shapes what economists view as reasonable explanations. Plausibility is evaluated within these norms of the scientific community and modeling practice.

Here are the key differences between model experiments and laboratory experiments:

Model experiments are conducted within the conceptual world of a theoretical or mathematical model. They involve manipulating variables and parameters within the model to examine outcomes and test implications.

Laboratory experiments involve real-world objects, phenomena or people in a controlled experimental setting. The goal is to test hypotheses or theories from the real world, not just within a model.

Models provide an idealized, simplified representation of reality while stripping out complicating factors. Laboratory experiments aim to reintroduce some of these real-world complexities in a controlled way.

The scientist has full control over the model and can change any assumptions at will. In the laboratory, there are limits to how closely experiments can mimic reality or isolate variables.

Findings from model experiments apply directly to that model system. Laboratory experiments aim to draw inferences about phenomena in the real world based on experimental analogues.

Model experiments explore logical implications, while laboratory experiments test empirically if theories hold under manipulation and observation of real processes.

Limitations of model experiments include the "inference gap" between model implications and real-world accuracy. Laboratory experiments help validate whether model logic applies to reality.

Here is a summary of the key points:

Hybrid experiments and simulations aim to combine aspects of real-world laboratory experiments with abstract theoretical modeling to gain insights that neither approach could achieve alone.

By integrating human participants and a mathematical market model, Hommes and Sonnemans were able to explore complex dynamic behaviors beyond what lab experiments or standard models allowed.

Participants made predictions that drove prices in the model, facing realistic noise and variations over runs, while the model structure linked participants and calculated outcomes.

This situated the experiment at the boundary between lab and pure modeling approaches, incorporating elements of both to study an issue neither could fully address separately.

Later work by Hommes and others pushed these hybrids closer to pure computer simulations by substituting mathematical decision rules for human participants.

Effectively, such hybrids represent an attempt to balance experimental control with freedom of behavior, and gain benefits of empirical richness while retaining a theoretical framework, to further economic understanding.

Here is a summary of the key points from the excerpt:

Clarkson and Simon argued that computer simulations allowed economists to model individual decision-making processes more directly, without first having to develop a complex mathematical model. Simulations could directly program symbolic representations of decision-making logic.

Their approach required fewer translations or transformations between the conceptual decision process and its implementation in the simulation. This provided a more valid test of hypotheses about individual decision-making.

By simulating individuals making sequential decisions over time in response to their environment, aggregate patterns could emerge from the bottom-up without being programmed in explicitly. This matched realities of complex decision processes.

It was a broader conception of simulation as an experimental tool for social science, rather than just numerical solving of mathematical models. The computer facilitated direct representation and experimental manipulation of conceptual models.

This reflects Clarkson and Simon's perspective that computer simulations were useful exploratory tools to generate and test hypotheses, complementing but not replacing traditional mathematical modeling.

Here is a summary of the key points:

The article discusses the use of game theory to model strategic situations, using the example of the opera Tosca. In Tosca, the characters of Tosca and Scarpia find themselves in a prisoner's dilemma-like scenario.

Their choices can be represented using game theory matrices showing the payoffs of different outcomes if they cooperate vs defect. Neither trusts the other to cooperate, so both choose to defect for selfish reasons even though cooperation would yield a better joint outcome.

This mirrors the adversarial nature of nuclear strategy during the Cold War. Both the US and USSR rationally chose not to cooperate and potentially bomb first due to distrust, even though mutual non-use of nukes would be preferable.

Rapoport used this analogy to criticize purely rational, game theory-based approaches to nuclear strategy and diplomatic relations. The prisoner's dilemma dynamic could lead countries to defect even when cooperation may be best.

His cartoon highlighted the limitations of game theory - in real-world strategic situations like the Cold War, psychological and emotional factors like trust were important but not fully captured by rational choice models. Cooperation requires building trust more than rational calculation alone.

So in summary, the article used the example of Tosca to illustrate how prisoner's dilemma dynamics can emerge in international relations, and criticized overly rational approaches to strategy that ignore important non-rational elements like trust.

Here is a summary of the key points about game theory and the use of narratives:

Game theory models describe stylized strategic situations using structures like payoff matrices. Narratives accompany these models to characterize the situation and assumptions, like rationality.

Narratives help translate real-world economic cases into game theory models by describing the elements and relationships involved. They also link the model back to analogous real situations.

This process allows economists to move flexibly between discussing an abstract model and applying it to a specific case. It blurs the distinction between analyzing using a model versus applying a model.

Over time, proliferation of special cases and variations undermined the goal of providing generalizable explanations through typical situations. However, individual models remained insightful examples.

Narratives play an important role in defining game models, providing explanatory depth when applying models, and allowing for richer reasoning about economic rationality and behavior. But they also contribute to the loss of broader theoretical coherence.

In summary, narratives are key to connecting game theory to economic analysis but also complicate attempts to develop unified theory from these strategic situation models.

Here is a summary:

Economic models are used both representatively (as models of the economy) and interventionistically (as models for shaping economic policy/outcomes).

As models of the economy, they provide simplified representations of economic phenomena to understand mechanisms and develop theoretical insights.

As models for intervention, they offer more operational frameworks that can directly inform specific policy decisions, private choices, or strategic behaviors with intent of manipulating outcomes.

There has been a shift toward more interventionary uses of models as economics has taken on more of an applied, engineering role focused on stewardship of the economy in addition to analysis.

However, the ability of models to fully control outcomes is limited since the economy also interacts with and changes models over time through feedback effects of real-world data and changes.

Both representational and interventionary uses rely on idealizing assumptions, and there are also tensions between generalizability of insights vs. accounting for context-dependent factors.

So in summary, economic models serve dual representational and interventionary functions that have influenced both the theoretical and applied roles of economics.

Here is a summary of the key points:

Models have become increasingly influential tools for economists to understand and intervene in the real world. Early economic theories tended to remain at an abstract, theoretical level.

Recent studies have shown how certain economic models, like Black-Scholes and auction/resource models, have been directly applied or "performatively" used to shape actual markets and policies.

Popular works translating economics to broader audiences, like Freakonomics, help disseminate economic analysis and explanations based on models to a wide range of people.

Models now integrate positive and normative elements more closely than before, allowing economists to more directly propose how they think the world should work according to their models. Models act as "embodiments of purpose" that can influence economic behavior.

By developing models to represent the economic system, economists have come to interpret and interact with the real world through the lens of those models. Their models have effectively reshaped how economists understand and engage with the real world.

In summary, the passage discusses how economic models have increasingly been used as tools by economists to both understand and actively intervene in markets and policy, demonstrating the performative power of models to reshape economic reality.