ECONOMICS OF INFORMATION: The Value and Impact of Knowledge

By Staff Reporters

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The economics of information explores how knowledge—or the lack of it—affects decision-making, market behavior, and resource allocation. It reveals why perfect competition rarely exists and why information itself can be a powerful economic asset.

Economics of Information: Understanding the Value and Impact of Knowledge

In traditional economic models, markets are often assumed to operate under perfect information—where all participants have equal access to relevant data. However, in reality, information is often incomplete, asymmetric, or costly to obtain. The field known as economics of information emerged to address these discrepancies, fundamentally reshaping how economists understand markets, incentives, and efficiency.

One of the core concepts in this field is information asymmetry, where one party in a transaction possesses more or better information than the other. This imbalance can lead to adverse selection and moral hazard. For example, in the insurance market, individuals who know they are high-risk are more likely to seek coverage, while insurers may struggle to differentiate between high- and low-risk clients. Similarly, in lending, borrowers may have private knowledge about their ability to repay, which lenders cannot easily verify.

To mitigate these problems, economists have developed mechanisms such as signaling and screening. Signaling occurs when the informed party takes action to reveal their type—like a job applicant earning a degree to signal competence. Screening, on the other hand, involves the uninformed party designing tests or contracts to elicit information—such as offering different insurance packages to separate risk levels.

Another important area is the cost of acquiring information. Gathering data, analyzing trends, or verifying facts requires time and resources. This leads to decisions being made under uncertainty, where individuals rely on heuristics or limited data. The economics of information examines how these costs influence behavior, pricing, and market structure. For instance, consumers may not compare every available product due to search costs, allowing firms to maintain price dispersion.

The rise of digital technology has intensified the relevance of this field. In the age of big data, companies like Google and Amazon thrive by collecting and analyzing vast amounts of user information. This data allows them to personalize services, predict behavior, and gain competitive advantages. However, it also raises concerns about privacy, market power, and inequality—issues that economists of information are increasingly addressing.

Moreover, information goods—such as software, media, and research—have unique economic properties. They are often non-rivalrous and can be reproduced at near-zero marginal cost. This challenges traditional pricing models and calls for innovative approaches like freemium strategies, bundling, and subscription services.

In public policy, the economics of information plays a crucial role in designing regulations, transparency standards, and consumer protections. Governments must balance the need for open access to information with incentives for innovation and investment. For example, patent laws aim to encourage research by granting temporary monopolies, while disclosure requirements in finance promote market integrity.

In conclusion, the economics of information reveals that knowledge is not just a passive input but a dynamic force shaping economic outcomes. By understanding how information is produced, distributed, and used, economists can better explain real-world phenomena and design systems that promote fairness, efficiency, and innovation.

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Understanding Polymaths, Savants, and Geniuses

By Staff Reporters

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What’s a polymath?

The definition of “polymath” is the subject of debate. The term has its roots in Ancient Greek and was first used in the early 17th Century to mean a person with “many learnings”, but there is no easy way to decide how advanced those learnings must be and in how many disciplines. Most researchers argue that to be a true polymath you need some kind of formal acclaim in at least two apparently unrelated domains. And, one of the most detailed examinations of the subject comes from Waqas Ahmed in his book The Polymath, published earlier this year.

Now, despite his many achievements, Ahmed does not identify as a polymath. “It is too esteemed an accolade for me to refer to myself as one,” he said. When examining the lives of historical polymaths, he only considered those who had made significant contributions to at least three fields, such as Leonardo da Vinci (the artist, inventor and anatomist), Johann Wolfgang von Goethe (the great writer who also studied botany, physics and mineralogy) and Florence Nightingale (who, besides founding modern nursing, was also an accomplished statistician and theologian).

What is a savant?

Savant syndrome is an exceedingly rare condition in which individuals with a developmental disorder or an intellectual disability possess extraordinary talents, knowledge, or abilities in a specific area. Savant syndrome may be congenital at birth or acquired later in life and is commonly associated with autism spectrum disorder (ASD). It may also coexist alongside other conditions, such as brain injuries . Individuals with savant syndrome were historically referred to with the term “idiot savant,” but negative connotations of the term “idiot” resulted in its abandonment and is now solely termed “savant.”

Famous individuals with savant syndrome include Kim Peek, who was able to calculate dates for any event hundreds of years into the past or future and inspired the movie the Rain Man. Stephen Wiltshire was mute and communicated through drawings of detailed city landscapes. Approximately 10% of individuals with autistic disorder have savant abilities. Less than 1% of the non-autistic population have savant syndrome. Therefore, not all savants have ASD, and not all persons with autismare savants.

What is a genius?

There is no scientifically precise definition of genius. When used to refer to the characteristic, genius is associated with talent but several authors systematically distinguish these terms. Walter Isaacson, biographer of many well-known geniuses, explains that although high intelligence may be a prerequisite, the most common trait that actually defines a genius may be the extraordinary ability to apply creativity and imaginative thinking to almost any situation.

The plural form of genius can be either geniuses or genii, pronounced [ jee-nee-ahy ], depending on the intended meaning of the word. Geniuses is much more commonly used. The plural forms of several other singular words that end in -us are also formed in this way, such as virus/viruses, callus/calluses, and status/statuses. Irregular plurals that are formed like genii, such as radius/radii or cactus/cacti, derive directly from their original pluralization in Latin. However, the standard English plural -es is often also acceptable for these terms, as in radiuses and cactuses.

Who is Mensa material?

Mensa members range in age from 2 to 106. They include engineers, homemakers, teachers, actors, athletes, students, and CEOs, and they share only one trait — high intelligence. To qualify for Mensa, they scored in the top 2 percent of the general population on an accepted standardized intelligence test.

 Note: These descriptions are presented with some thanks to Chat GPT.

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Understanding Machine Learning Distillation

DEFINITION

By Staff Reporters

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What is distillation? In machine learning, distillation is a technique for transferring knowledge from a large, complex model (often called the teacher model) to a smaller, simpler model (the student model). This process helps the smaller model achieve similar performance to the larger one while being more efficient in terms of computation and memory usage.

Distillation steps: The main steps in knowledge distillation are: [1.] Train the student model by using these predictions, along with the original dataset, to mimic the teacher model’s behavior. And, [2.] use the teacher model to generate predictions for the dataset.

Cite: ChatGPT via MSFT

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PODCAST: What is Epistemic Ambivalence?

Epistemic Ambivalence!

CITE: https://www.r2library.com/Resource/Title/0826102549

[By staff reporters]

Epistemic Ambivalence is almost the opposite idea of what ambivalence means because to be epistemic means you know, you are sure.

Epistemic ambivalence is when you may know the truth of a situation but cannot say which truth it is, because there is more than one option.

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MORE: Schrödinger’s cat is a thought experiment, sometimes described as a paradox, devised by Austrian physicist Erwin Schrödinger in 1935. It illustrates what he saw as the problem of the Copenhagen interpretation of quantum mechanics applied to everyday objects. The scenario presents a hypothetical cat that may be simultaneously both alive and dead, a state known as a quantum superposition, as a result of being linked to a random subatomic event that may or may not occur. The thought experiment is also often featured in theoretical discussions of the interpretations of quantum mechanics. Schrödinger coined the term Verschränkung (entanglement) in the course of developing the thought experiment.
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Assessment: Your thoughts are appreciated.
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Comprehensive Financial Planning Strategies for Doctors and Advisors: Best Practices from Leading Consultants and Certified Medical Planners(TM)
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What is Knowledge?

A Binary Proposition in the Past – Not so much, Today? 

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By Dr. David E. Marcinko MBA

Common Knowledge is a familiarity, awareness or understanding of someone or something, such as facts, information, descriptions, or skills, which is acquired through experience or education by perceiving, discovering, or learning.

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LINK: https://en.wikipedia.org/wiki/Knowledge

The CURSE of Knowledge?

Medical Knowledge is the body of information about diseases, mechanisms and pathogenesis, therapies and interactions, and interpretation of lab tests, which is broadly applicable to decisions about multiple patients and public health policies, in contrast to patient-specific data.

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GOOGLE MD

LINK: https://en.wikipedia.org/wiki/Medicine

What is the Dunning–Kruger effect?

Assessment

Beware! No one person or medical specialty physician is immune! So, where do you fit in on this binary schematic?

Conclusion

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