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Tuesday, February 20, 2024

Mathematical model

From Wikipedia, the free encyclopedia

A mathematical model is an abstract description of a concrete system using mathematical concepts and language. The process of developing a mathematical model is termed mathematical modeling. Mathematical models are used in applied mathematics and in the natural sciences (such as physics, biology, earth science, chemistry) and engineering disciplines (such as computer science, electrical engineering), as well as in non-physical systems such as the social sciences (such as economics, psychology, sociology, political science). It can also be taught as a subject in its own right.

The use of mathematical models to solve problems in business or military operations is a large part of the field of operations research.

Mathematical models are also used in music, linguistics, and philosophy (for example, intensively in analytic philosophy). A model may help to explain a system and to study the effects of different components, and to make predictions about behavior.

Elements of a mathematical model

Mathematical models can take many forms, including dynamical systems, statistical models, differential equations, or game theoretic models. These and other types of models can overlap, with a given model involving a variety of abstract structures. In general, mathematical models may include logical models. In many cases, the quality of a scientific field depends on how well the mathematical models developed on the theoretical side agree with results of repeatable experiments. Lack of agreement between theoretical mathematical models and experimental measurements often leads to important advances as better theories are developed.

In the physical sciences, a traditional mathematical model contains most of the following elements:

  1. Governing equations
  2. Supplementary sub-models
    1. Defining equations
    2. Constitutive equations
  3. Assumptions and constraints
    1. Initial and boundary conditions
    2. Classical constraints and kinematic equations

Classifications

Mathematical models are of different types:

  • Linear vs. nonlinear: If all the operators in a mathematical model exhibit linearity, the resulting mathematical model is defined as linear. A model is considered to be nonlinear otherwise. The definition of linearity and nonlinearity is dependent on context, and linear models may have nonlinear expressions in them. For example, in a statistical linear model, it is assumed that a relationship is linear in the parameters, but it may be nonlinear in the predictor variables. Similarly, a differential equation is said to be linear if it can be written with linear differential operators, but it can still have nonlinear expressions in it. In a mathematical programming model, if the objective functions and constraints are represented entirely by linear equations, then the model is regarded as a linear model. If one or more of the objective functions or constraints are represented with a nonlinear equation, then the model is known as a nonlinear model.
    Linear structure implies that a problem can be decomposed into simpler parts that can be treated independently and/or analyzed at a different scale and the results obtained will remain valid for the initial problem when recomposed and rescaled.
    Nonlinearity, even in fairly simple systems, is often associated with phenomena such as chaos and irreversibility. Although there are exceptions, nonlinear systems and models tend to be more difficult to study than linear ones. A common approach to nonlinear problems is linearization, but this can be problematic if one is trying to study aspects such as irreversibility, which are strongly tied to nonlinearity.
  • Static vs. dynamic: A dynamic model accounts for time-dependent changes in the state of the system, while a static (or steady-state) model calculates the system in equilibrium, and thus is time-invariant. Dynamic models typically are represented by differential equations or difference equations.
  • Explicit vs. implicit: If all of the input parameters of the overall model are known, and the output parameters can be calculated by a finite series of computations, the model is said to be explicit. But sometimes it is the output parameters which are known, and the corresponding inputs must be solved for by an iterative procedure, such as Newton's method or Broyden's method. In such a case the model is said to be implicit. For example, a jet engine's physical properties such as turbine and nozzle throat areas can be explicitly calculated given a design thermodynamic cycle (air and fuel flow rates, pressures, and temperatures) at a specific flight condition and power setting, but the engine's operating cycles at other flight conditions and power settings cannot be explicitly calculated from the constant physical properties.
  • Discrete vs. continuous: A discrete model treats objects as discrete, such as the particles in a molecular model or the states in a statistical model; while a continuous model represents the objects in a continuous manner, such as the velocity field of fluid in pipe flows, temperatures and stresses in a solid, and electric field that applies continuously over the entire model due to a point charge.
  • Deterministic vs. probabilistic (stochastic): A deterministic model is one in which every set of variable states is uniquely determined by parameters in the model and by sets of previous states of these variables; therefore, a deterministic model always performs the same way for a given set of initial conditions. Conversely, in a stochastic model—usually called a "statistical model"—randomness is present, and variable states are not described by unique values, but rather by probability distributions.
  • Deductive, inductive, or floating: A deductive model is a logical structure based on a theory. An inductive model arises from empirical findings and generalization from them. The floating model rests on neither theory nor observation, but is merely the invocation of expected structure. Application of mathematics in social sciences outside of economics has been criticized for unfounded models. Application of catastrophe theory in science has been characterized as a floating model.
  • Strategic vs non-strategic Models used in game theory are different in a sense that they model agents with incompatible incentives, such as competing species or bidders in an auction. Strategic models assume that players are autonomous decision makers who rationally choose actions that maximize their objective function. A key challenge of using strategic models is defining and computing solution concepts such as Nash equilibrium. An interesting property of strategic models is that they separate reasoning about rules of the game from reasoning about behavior of the players.

Construction

In business and engineering, mathematical models may be used to maximize a certain output. The system under consideration will require certain inputs. The system relating inputs to outputs depends on other variables too: decision variables, state variables, exogenous variables, and random variables.

Decision variables are sometimes known as independent variables. Exogenous variables are sometimes known as parameters or constants. The variables are not independent of each other as the state variables are dependent on the decision, input, random, and exogenous variables. Furthermore, the output variables are dependent on the state of the system (represented by the state variables).

Objectives and constraints of the system and its users can be represented as functions of the output variables or state variables. The objective functions will depend on the perspective of the model's user. Depending on the context, an objective function is also known as an index of performance, as it is some measure of interest to the user. Although there is no limit to the number of objective functions and constraints a model can have, using or optimizing the model becomes more involved (computationally) as the number increases.

For example, economists often apply linear algebra when using input–output models. Complicated mathematical models that have many variables may be consolidated by use of vectors where one symbol represents several variables.

A priori information

To analyse something with a typical "black box approach", only the behavior of the stimulus/response will be accounted for, to infer the (unknown) box. The usual representation of this black box system is a data flow diagram centered in the box.

Mathematical modeling problems are often classified into black box or white box models, according to how much a priori information on the system is available. A black-box model is a system of which there is no a priori information available. A white-box model (also called glass box or clear box) is a system where all necessary information is available. Practically all systems are somewhere between the black-box and white-box models, so this concept is useful only as an intuitive guide for deciding which approach to take.

Usually, it is preferable to use as much a priori information as possible to make the model more accurate. Therefore, the white-box models are usually considered easier, because if you have used the information correctly, then the model will behave correctly. Often the a priori information comes in forms of knowing the type of functions relating different variables. For example, if we make a model of how a medicine works in a human system, we know that usually the amount of medicine in the blood is an exponentially decaying function. But we are still left with several unknown parameters; how rapidly does the medicine amount decay, and what is the initial amount of medicine in blood? This example is therefore not a completely white-box model. These parameters have to be estimated through some means before one can use the model.

In black-box models, one tries to estimate both the functional form of relations between variables and the numerical parameters in those functions. Using a priori information we could end up, for example, with a set of functions that probably could describe the system adequately. If there is no a priori information we would try to use functions as general as possible to cover all different models. An often used approach for black-box models are neural networks which usually do not make assumptions about incoming data. Alternatively, the NARMAX (Nonlinear AutoRegressive Moving Average model with eXogenous inputs) algorithms which were developed as part of nonlinear system identification can be used to select the model terms, determine the model structure, and estimate the unknown parameters in the presence of correlated and nonlinear noise. The advantage of NARMAX models compared to neural networks is that NARMAX produces models that can be written down and related to the underlying process, whereas neural networks produce an approximation that is opaque.

Subjective information

Sometimes it is useful to incorporate subjective information into a mathematical model. This can be done based on intuition, experience, or expert opinion, or based on convenience of mathematical form. Bayesian statistics provides a theoretical framework for incorporating such subjectivity into a rigorous analysis: we specify a prior probability distribution (which can be subjective), and then update this distribution based on empirical data.

An example of when such approach would be necessary is a situation in which an experimenter bends a coin slightly and tosses it once, recording whether it comes up heads, and is then given the task of predicting the probability that the next flip comes up heads. After bending the coin, the true probability that the coin will come up heads is unknown; so the experimenter would need to make a decision (perhaps by looking at the shape of the coin) about what prior distribution to use. Incorporation of such subjective information might be important to get an accurate estimate of the probability.

Complexity

In general, model complexity involves a trade-off between simplicity and accuracy of the model. Occam's razor is a principle particularly relevant to modeling, its essential idea being that among models with roughly equal predictive power, the simplest one is the most desirable. While added complexity usually improves the realism of a model, it can make the model difficult to understand and analyze, and can also pose computational problems, including numerical instability. Thomas Kuhn argues that as science progresses, explanations tend to become more complex before a paradigm shift offers radical simplification.

For example, when modeling the flight of an aircraft, we could embed each mechanical part of the aircraft into our model and would thus acquire an almost white-box model of the system. However, the computational cost of adding such a huge amount of detail would effectively inhibit the usage of such a model. Additionally, the uncertainty would increase due to an overly complex system, because each separate part induces some amount of variance into the model. It is therefore usually appropriate to make some approximations to reduce the model to a sensible size. Engineers often can accept some approximations in order to get a more robust and simple model. For example, Newton's classical mechanics is an approximated model of the real world. Still, Newton's model is quite sufficient for most ordinary-life situations, that is, as long as particle speeds are well below the speed of light, and we study macro-particles only.

Note that better accuracy does not necessarily mean a better model. Statistical models are prone to overfitting which means that a model is fitted to data too much and it has lost its ability to generalize to new events that were not observed before.

Training, tuning, and fitting

Any model which is not pure white-box contains some parameters that can be used to fit the model to the system it is intended to describe. If the modeling is done by an artificial neural network or other machine learning, the optimization of parameters is called training, while the optimization of model hyperparameters is called tuning and often uses cross-validation. In more conventional modeling through explicitly given mathematical functions, parameters are often determined by curve fitting.

Evaluation and assessment

A crucial part of the modeling process is the evaluation of whether or not a given mathematical model describes a system accurately. This question can be difficult to answer as it involves several different types of evaluation.

Prediction of empirical data

Usually, the easiest part of model evaluation is checking whether a model predicts experimental measurements or other empirical data not used in the model development. In models with parameters, a common approach is to split the data into two disjoint subsets: training data and verification data. The training data are used to estimate the model parameters. An accurate model will closely match the verification data even though these data were not used to set the model's parameters. This practice is referred to as cross-validation in statistics.

Defining a metric to measure distances between observed and predicted data is a useful tool for assessing model fit. In statistics, decision theory, and some economic models, a loss function plays a similar role.

While it is rather straightforward to test the appropriateness of parameters, it can be more difficult to test the validity of the general mathematical form of a model. In general, more mathematical tools have been developed to test the fit of statistical models than models involving differential equations. Tools from nonparametric statistics can sometimes be used to evaluate how well the data fit a known distribution or to come up with a general model that makes only minimal assumptions about the model's mathematical form.

Scope of the model

Assessing the scope of a model, that is, determining what situations the model is applicable to, can be less straightforward. If the model was constructed based on a set of data, one must determine for which systems or situations the known data is a "typical" set of data.

The question of whether the model describes well the properties of the system between data points is called interpolation, and the same question for events or data points outside the observed data is called extrapolation.

As an example of the typical limitations of the scope of a model, in evaluating Newtonian classical mechanics, we can note that Newton made his measurements without advanced equipment, so he could not measure properties of particles traveling at speeds close to the speed of light. Likewise, he did not measure the movements of molecules and other small particles, but macro particles only. It is then not surprising that his model does not extrapolate well into these domains, even though his model is quite sufficient for ordinary life physics.

Philosophical considerations

Many types of modeling implicitly involve claims about causality. This is usually (but not always) true of models involving differential equations. As the purpose of modeling is to increase our understanding of the world, the validity of a model rests not only on its fit to empirical observations, but also on its ability to extrapolate to situations or data beyond those originally described in the model. One can think of this as the differentiation between qualitative and quantitative predictions. One can also argue that a model is worthless unless it provides some insight which goes beyond what is already known from direct investigation of the phenomenon being studied.

An example of such criticism is the argument that the mathematical models of optimal foraging theory do not offer insight that goes beyond the common-sense conclusions of evolution and other basic principles of ecology.

It should also be noted that while mathematical modeling uses mathematical concepts and language, it is not itself a branch of mathematics and does not necessarily conform to any mathematical logic, but is typically a branch of some science or other technical subject, with corresponding concepts and standards of argumentation.

Significance in the natural sciences

Mathematical models are of great importance in the natural sciences, particularly in physics. Physical theories are almost invariably expressed using mathematical models.

Throughout history, more and more accurate mathematical models have been developed. Newton's laws accurately describe many everyday phenomena, but at certain limits theory of relativity and quantum mechanics must be used.

It is common to use idealized models in physics to simplify things. Massless ropes, point particles, ideal gases and the particle in a box are among the many simplified models used in physics. The laws of physics are represented with simple equations such as Newton's laws, Maxwell's equations and the Schrödinger equation. These laws are a basis for making mathematical models of real situations. Many real situations are very complex and thus modeled approximately on a computer, a model that is computationally feasible to compute is made from the basic laws or from approximate models made from the basic laws. For example, molecules can be modeled by molecular orbital models that are approximate solutions to the Schrödinger equation. In engineering, physics models are often made by mathematical methods such as finite element analysis.

Different mathematical models use different geometries that are not necessarily accurate descriptions of the geometry of the universe. Euclidean geometry is much used in classical physics, while special relativity and general relativity are examples of theories that use geometries which are not Euclidean.

Some applications

Often when engineers analyze a system to be controlled or optimized, they use a mathematical model. In analysis, engineers can build a descriptive model of the system as a hypothesis of how the system could work, or try to estimate how an unforeseeable event could affect the system. Similarly, in control of a system, engineers can try out different control approaches in simulations.

A mathematical model usually describes a system by a set of variables and a set of equations that establish relationships between the variables. Variables may be of many types; real or integer numbers, Boolean values or strings, for example. The variables represent some properties of the system, for example, the measured system outputs often in the form of signals, timing data, counters, and event occurrence. The actual model is the set of functions that describe the relations between the different variables.

Examples

  • One of the popular examples in computer science is the mathematical models of various machines, an example is the deterministic finite automaton (DFA) which is defined as an abstract mathematical concept, but due to the deterministic nature of a DFA, it is implementable in hardware and software for solving various specific problems. For example, the following is a DFA M with a binary alphabet, which requires that the input contains an even number of 0s:
The state diagram for
where
  • and
  • is defined by the following state-transition table:

0
1
S1
S2
The state represents that there has been an even number of 0s in the input so far, while signifies an odd number. A 1 in the input does not change the state of the automaton. When the input ends, the state will show whether the input contained an even number of 0s or not. If the input did contain an even number of 0s, will finish in state an accepting state, so the input string will be accepted.
The language recognized by is the regular language given by the regular expression 1*( 0 (1*) 0 (1*) )*, where "*" is the Kleene star, e.g., 1* denotes any non-negative number (possibly zero) of symbols "1".
  • Many everyday activities carried out without a thought are uses of mathematical models. A geographical map projection of a region of the earth onto a small, plane surface is a model which can be used for many purposes such as planning travel.
  • Another simple activity is predicting the position of a vehicle from its initial position, direction and speed of travel, using the equation that distance traveled is the product of time and speed. This is known as dead reckoning when used more formally. Mathematical modeling in this way does not necessarily require formal mathematics; animals have been shown to use dead reckoning.
  • Population Growth. A simple (though approximate) model of population growth is the Malthusian growth model. A slightly more realistic and largely used population growth model is the logistic function, and its extensions.
  • Model of a particle in a potential-field. In this model we consider a particle as being a point of mass which describes a trajectory in space which is modeled by a function giving its coordinates in space as a function of time. The potential field is given by a function and the trajectory, that is a function is the solution of the differential equation:
    that can be written also as
Note this model assumes the particle is a point mass, which is certainly known to be false in many cases in which we use this model; for example, as a model of planetary motion.
  • Model of rational behavior for a consumer. In this model we assume a consumer faces a choice of commodities labeled each with a market price The consumer is assumed to have an ordinal utility function (ordinal in the sense that only the sign of the differences between two utilities, and not the level of each utility, is meaningful), depending on the amounts of commodities consumed. The model further assumes that the consumer has a budget which is used to purchase a vector in such a way as to maximize The problem of rational behavior in this model then becomes a mathematical optimization problem, that is:
    subject to:
    This model has been used in a wide variety of economic contexts, such as in general equilibrium theory to show existence and Pareto efficiency of economic equilibria.
  • Neighbour-sensing model is a model that explains the mushroom formation from the initially chaotic fungal network.
  • In computer science, mathematical models may be used to simulate computer networks.
  • In mechanics, mathematical models may be used to analyze the movement of a rocket model.

Truth Decay (book)

From Wikipedia, the free encyclopedia
 
Truth Decay
AuthorJennifer Kavanagh and Michael Rich
CountryUnited States
LanguageEnglish
GenreNon-fiction
PublisherRAND Corporation
Publication date
January 16, 2018

Truth Decay is a non-fiction book by Jennifer Kavanagh and Michael D. Rich. Published by the RAND Corporation on January 16, 2018, the book examines historical trends such as "yellow journalism" and "new journalism" to demonstrate that "truth decay" is not a new phenomenon in American society. The authors argue that the divergence between individuals over objective facts and the concomitant increase in the relative "volume and influence of opinion over fact" in civil and political discourse has historically proliferated American society and culminated in truth decay.

The term "truth decay" was suggested by Sonni Efron and adopted by the authors of the book to characterize four interrelated trends in American society.

Kavanagh and Rich describe the "drivers" of truth decay as cognitive prejudices, transformation of information systems, competing demands on the education system, and polarization. This has consequences on various aspects of American society. The authors argue that truth decay has engendered the deterioration of "civil discourse" and "politica paralysis". This has culminated in an increasing withdrawal of individuals from institutional sites of discourse throughout modern American society.

Truth Decay was positively received by audiences. The book was a nonfiction bestseller in the United States. Indeed, Barack Obama included the "very interesting" book in his 2018 reading list. Further, it stimulated a panel discussion at the University of Sydney on the role of media institutions in society and the ways in which democratic governance and civic engagement can be improved.

Publishing history

Truth Decay was first published as a web-only book on January 16, 2018, by the RAND corporation. This allowed individuals to read the book online without incurring any costs. On 26 January 2018, physical copies of the book were also published by the RAND corporation and made available for order on websites such as Amazon and Apple Books.

The RAND corporation is a non-profit and nonpartisan research organization that is based in California.[8] It is concerned about the social, economic and political dangers that truth decay poses to the decision making processes of individuals in society. Kavanagh, a senior political scientist, has expressed concern that there is an increasing number of people in America and Europe are doubtful of climate change and the efficacy of vaccines.

The term truth decay

In Chapter 1, Kavanagh and Rich introduce the term “truth decay”. The term “truth decay” was suggested by Sonni Efron and adopted by the authors of the book to characterize four interrelated trends in American society, including:

  • Increasing differences between individuals about objective facts;
  • Increasing conflation of opinion and fact in discourse;
  • Increasing quantity and authority of opinion rather than fact in discourse; and
  • Diminishing faith in traditionally authoritative sources of reliable and accurate information.

Kavanagh and Rich differentiate truth decay from “fake news”. The authors argue that phenomena such as “fake news” have not, in themselves, catalyzed the shift away from objective facts in political and civil discourse. The authors allege that “fake news” constitutes an aspect of truth decay and the associated challenges arising from the diminishing faith in historically authoritative sources of accurate information such as government, media and education. Notwithstanding this distinction, the authors argue that the expression “fake news” has been intentionally deployed by politicians such as Donald Trump and Vladimir Putin to diminish the accuracy and facticity of information promulgated by sources that do not align with their partisan position. In that context, the authors argue that a limited focus on phenomena such as “fake news” inhibits a vigorous analysis of the causes and consequences of truth decay in society.

Structure and major arguments

Truth Decay is organized in six chapters and explores three historical eras — the 1890s, 1920s, and 1960s — for historical evidence of the four trends of Truth Decay. The authors argue that Truth Decay is “not a new phenomenon” as there has been a sustained increase in the volume and influence of opinion over fact throughout the last century.

Historical context

In Chapter 3, the book explores three eras — the 1890s, 1920s, and 1960s — for historical evidence of the aforementioned four trends of truth decay in American society.

Gilded Age

Depiction of a young woman being strip-searched by imposing Spanish policemen (Illustrator: Frederic Remington)

First, the authors identify the 1880s–1890s as the "Gilded Age". This historical era commenced after the American Civil War and was punctuated by the industrialization of America. The introduction of printing technology increased the output of newspaper publishers. This stimulated competition within the newspaper publishing industry. In New York City, major newspaper publishers Joseph Pulitzer and William Hearst engaged in "yellow journalism" by deploying a sensationalist style of covering politics, world events and crime in order to fend off competitors and attract market share. The authors note that these publishers also deployed "yellow journalism" to advance the partisan political objectives of their respective news organizations. For example, in April 1898, the New York Journal owned by Hearst published a number of articles with bold headlines, violent images and aggrandized information to position the Cubans as "innocent" people being "persecuted by the illiberal Spanish" regime and thereby emphasize the propriety of America's intervention in the Spanish-American War to the audience. Thus, "yellow journalism" caused a conflation of opinions and objectively verifiable facts in society.

Roaring Twenties and the Great Depression

Second, the authors identify the 1920s–1930s as the Roaring Twenties and the Great Depression. This historical era was renowned as another period of economic growth and development that catalysed significant changes in the American media industry. The authors argue that radio broadcasting and tabloid journalism emerged as a dramatized form of media that focused on news surrounding public figures such as politicians, actors, musicians and sports athletes as entertainment rather than reliable and accurate information for the audience to utilise in considered decision-making. As such, "jazz journalism" is alleged to have amplified the conflation of opinions and objectively verifiable facts in society.

The Civil Rights Movement

Third, the authors identify the 1960s–1970s as the period of "civil rights and social unrest". This historical era was punctuated by America's involvement in the Vietnam War. Television news was used to disseminate information which portrayed the appropriateness and success of America's involvement in the Vietnam War to the audience. Kavanagh and Rich argue that this increasingly conflated opinion and objective facts to advance partisan objectives. The Civil Rights movement in the 1960s contributed to a transformation in news reporting. Journalists began to deploy first-person narration in their reporting of world events to illuminate the inequities faced by African American citizens who strived for recognition and civil rights. On its face, this incidence of "new journalism" increased the risk of reporters imbuing their work with personal biases. Nonetheless, Bainer suggests that "new journalism" also augmented reporting as it permitted journalists to disseminate information on matters without the hollow pretence of objective reporting.

Current drivers

In Chapter 4, Kavanagh and Rich describe the "drivers" of the aforementioned four trends of truth decay as cognitive prejudices, transformation of information systems and cuts to the education sector.

Cognitive prejudices

First, cognitive prejudices are described as systematic errors in rational thinking that transpire when individuals are absorbing information. Confirmation bias is the propensity to identify and prioritise information that supports a pre-existing worldview. This has a number of impacts on the process of individual decision-making. The authors argue that individuals consciously or unconsciously employ motivated reasoning to resist accepting information that challenges their pre-existing worldview. This causes the interface with invalidating information to further ingrain the partisan opinions of individuals. It is alleged by the authors that, in the long term, cognitive prejudices have created "political, sociodemographic, and economic polarisation" as individuals form cliques that are diametrically opposed in their worldview and communication, thereby attenuating the quality civil discourse in American society.

Transformation of information systems

Second, the transformation of information systems refers to the surge in the "volume and speed of news" that is disseminated to individuals. The authors note that the move towards a "24-hour news cycle" has increased the number of competitors to traditional news organizations. This competition, it is said, has reduced profitability and compelled news organizations such as ABC and Fox to pivot from costly investigative journalism to sensationalized opinion as a less-costly method of attracting an audience. The increase in the quantity of opinion rather than objectively discernible fact in reporting is further exacerbated by the introduction of social media platforms such as Twitter and Facebook. These social media platforms facilitate rapid access to, and dissemination of, opinion news to millions of users.

Cuts to the educational sector

U.S. Federal Budget Deficit from 2018 to 2027

Third, the authors allege that cuts to the educational sector have catalyzed a reduction in the critical thinking and media literacy education of individuals. Kavanagh and Rich argue that individuals utilise the information and critical thinking skills established in traditionally authoritative sites of discourse such as secondary schools and universities to make decisions. Financial constraints associated with the swelling federal budget deficit from 2010 to 2021 have precipitated cuts to the funding apportioned to the American education sector. The authors argue that this has meant that, in the face of the increasing volume of online news, fewer students have acquired the technical and emotional skills to identify the explicit and implicit biases of reporters and thereby critically assess the accuracy and reliability of information emanating from sources such as the government and media. Ranschaert uses data gained through a longitudinal study of social studies teachers to argue that the decline in individuals relying on teachers for authoritative information has serious implications for ability of the education system to act as a buffer against truth decay. The authors go further than Ranschaert by arguing that, in the long term, this has resulted in a constituency that is vulnerable to absorbing and promoting misinformation as the skill to delineate objective facts from misinformation has atrophied. In that context, the disparity between the media literacy education of students and the challenges posed by Internet technology is said to engender truth decay.

Current consequences

In Chapter 5, Kavanagh and Rich describe the consequences of truth decay in America.

Deterioration of civil discourse in society

Violent protests at the Minnesota Capitol

First, it is alleged that truth decay manifests in the deterioration of civil discourse in modern American society. The authors define civil discourse as vigorous dialogue that attempts to promote the public interest. It follows that, in the absence of a baseline set of objective facts, the authors suggest that the ability for individuals and politicians to meaningfully listen and engage in a constructive dialogue about economics, science and policy is diminished.

Political paralysis

Second, truth decay is alleged to manifest in "political paralysis". The authors note that the deterioration of civil discourse and increasing dispute about objective facts has created a deep chasm between conservative and liberal politicians in America. A case study on the increasing use of the filibuster in the United States Senate between 1947 and 2017 is used to suggest that truth decay has culminated in conservative and liberal politicians being increasingly unable to compromise on a range of policy initiatives. This incurs short term economic costs for the U.S. economy as the government becomes rigid an unable to respond promptly to domestic crises that require direct intervention. For example, America's federal government shut down in 2013 due to the inability of the Senate to pass the Affordable Care Act. The lack of funding for federal operations resulted in a $24 billion loss to the economy. In the long term, political paralysis also causes the U.S. to drop in international standing.

Withdrawal of individuals from institutional sites of discourse

Third, truth decay is alleged to have engendered the withdrawal of individuals from institutional sites of discourse. The authors argue that the decrease of faith in education institutions, media and government among young voters aged between 18 and 29 precipitated the consistent decrease in the overall number of votes cast in the U.S federal election from 2004 to 2016. This decrease in the exercise of civic responsibility through voting may, in the long run, diminish the ability of citizens to scrutinise state power, thereby diminishing policy making and overall accountability.

Reception

Truth Decay was positively received by American audiences. The book debuted as a Nonfiction Bestseller in 2018. On Amazon.com, the book is rated 4.3 stars out of 5 stars.

The book subsequently stimulated a panel discussion at the University of Sydney. On 22 August 2018, Michael Rich joined Professor Simon Jackman, John Barron, Nick Enfield and Lisa Bero for a discussion of the causes and consequences of truth decay in modern society. This panel was co-hosted by the RAND Australia and the United States Studies Centre.

Excerpts from the book were published by CNN, ABC and the Washington Post. An article on the ABC website reported on the "troubling trend" of truth decay which was "exposed" by the authors of the book.

Barack Obama included the "very interesting" book in his 2018 summer reading list. Obama noted that "a selective sorting of facts and evidence" is deceitful and corrosive to civil discourse. This is because "society has always worked best when reasoned debate and practical problem-solving thrive". This notion was echoed by Cãtãlina Nastasiu, who lauded the "ambitious exploratory work" because it "serves as a base to better understand the information ecosystem".

Institute for the Future

From Wikipedia, the free encyclopedia
https://en.wikipedia.org/wiki/Institute_for_the_Future
 
Institute for the Future
Company typeNot for profit
IndustryFuture Forecasting
Founded1968; 56 years ago
in Middletown, Connecticut, United States
FoundersFrank Davidson, Olaf Helmer, Paul Baran, Arnold Kramish, and Theodore Gordon
Headquarters201 Hamilton Avenue, ,
United States
Key people
Marina Gorbis
ServicesTen Year Forecast, Technology Horizons, Health Horizons
Websiteiftf.org

The Institute for the Future (IFTF) is a Palo Alto, California, US–based not-for-profit think tank. It was established, in 1968, as a spin-off from the RAND Corporation to help organizations plan for the long-term future, a subject known as futures studies.

History

Genesis

First references to the idea of an Institute for the Future may be found in a 1966 Prospectus by Olaf Helmer and others. While at RAND Corporation, Helmer had already been involved with developing the Delphi method of futures studies. He, and others, wished to extend the work further with an emphasis on examining multiple scenarios. This can be seen in the prospectus summary:

  • To explore systematically the possible futures for our [USA] nation and for the international community.
  • To ascertain which among these possible futures seems desirable, and why.
  • To seek means by which the probability of their occurrence can be enhanced through appropriate purposeful action.

First years

The Institute opened in 1968, in Middletown, Connecticut. The initial group was led by Frank Davidson and included Olaf Helmer, Paul Baran, Arnold Kramish, and Theodore Gordon.

The Institute's work initially relied on the forecasting methods built upon by Helmer while at RAND. The Delphi method was used to glean information from multiple anonymous sources. It was augmented by Cross Impact Analysis, which encouraged analysts to consider multiple future scenarios.

While precise and powerful, the methods that had been developed in a corporate environment were oriented to providing business and economic analyses. At a 1971 conference on mathematical modelling Helmer noted the need for similar improvements in societal modelling. Early attempts at doing so included a "Future State of the Union" report, formatted according to the traditional US Presidential address to the Nation.

Despite establishing an excellent reputation for painstaking analysis of future analyses and forecasting methods, various problems meant that the Institute struggled to find its footing at first. In 1970 Helmer took over the leadership from Davidson, and the Institute shifted its headquarters to Menlo Park, California.

In 1971 Roy Amara took over from Helmer, who continued to run the Middletown office until his departure in 1973.3 Amara held this position until 1990. During Amara's presidency, the Institute conducted some of the earliest studies of the impact of the ARPANET on collaborative work and scientific research, and was notable for its research on computer mediated communications, also known as groupware.

Starting from the early seventies astrophysicist and computer scientist Jacques Vallee, sociologist Bob Johansen, and technology forecaster Paul Saffo worked for IFTF.

An increase in corporate focus

In 1975 the Corporate Associates Program was started to assist private organisations interpret emerging trends and the long-term consequences. Although this program operated until 2001, its role as the Institute's main reporting tool was superseded by the Ten Year Forecast in 1978.

In 1984 the sociologist Herbert L Smith noted that, by the late 1970s, the idea of an open Union reporting format had given way to the proprietary Ten Year Forecast. Smith interpreted this as a renewed focus on business forecasting as public funds became scarce.

It is not clear how pertinent Smith's observations were to how the Institute was operating in this period. Sociologists such as Bob Johansen continued to be active in the Institute's projects. Having taken part in early ARPANET development, Institute staff were well aware of the impact that computer networking would have on society and its inclusion in policy making. However, in a 1984 essay, Roy Amara appeared to acknowledge some form of crisis, and a renewed interest in societal forecasting.

Evolution of societal forecasting

New ways of presenting studies to a less specialised audience were adopted, or developed. As an aid to memory retention, 'Vignetting' presented future scenarios as short stories; to illustrate the point of the scenario, and engage the reader's attention. Later initiatives showed an increasing emphasis on narrative engagement, e.g. 'Artifacts of the future', and 'Human-future interaction'.

Ethnographic forecasting was adopted as it became recognised that "society" was actually a myriad of sub-cultures, each with its own outlook.

While older forecasting methods sought the advice of field experts, newer techniques sought the statistical input from all members of society. Public interaction, provided via the internet and social media, made it possible to engage in "bottom up forecasting". While roleplaying and simulation games had long been part of a forecaster's tools, they could now be scaled up into "massively multiplayer forecasting games" such as Superstruct. This game enlisted the blogs and wikis of over 5,000 people to discuss life 10 years in the future; presenting them with a set of hypothetical, overlapping social threats, and encouraging them to seek collaborative "superstruct" solutions. The concept of the superstruct was subsequently incorporated into the Institute's 'Foresight Engine' tool.

Work

The Institute maintains research programs on the futures of technology, health, and organizations. It publishes a variety of reports and maps, as well as Future Now, a blog on emerging technologies. It offers three programs to its clients:

  • The Ten year forecast is the Institute's signature piece, having operated since 1978. It tracks today's latent signals, and forecasts what they might mean for business in ten years' time.
  • The Technology Horizons program, beginning around 2004,escribed by the Institute as "combining a deep understanding of technology and societal forces to identify and evaluate discontinuities and innovations in the next three to ten years".
  • The Health horizons program has operated since 2005. The Institute describes its purpose as "seeking more resilient responses for the complex challenges facing global health".

In 2014 the Institute moved its headquarters to 201 Hamilton Avenue, Palo Alto, California.

The Institute's annual publication Future Now is intended to provide summaries of the Institute's body of research. The inaugural edition was published in February 2017. Its theme The New Body Language concentrated on the Technology Horizons Program's studies on human and machine symbiosis.

People

Marina Gorbis, 2013

As of 2016 the Institute's executive director is Marina Gorbis. Also associated with the institute are David Pescovitz, Anthony M. Townsend, Jane McGonigal, and Jamais Cascio.

Past leaders

  • Frank Davidson (1968–70)
  • Olaf Helmer (1970)
  • Roy Amara (1971–90)
  • Ian Morrison (1990–96)
  • Bob Johansen (1996–2004)
  • Peter Banks (2004–06)
  • Marina Gorbis (2006–)

Ratings

Guidestar, the largest information source on nonprofit organizations and private foundations in the United States, gave Institute for the Future a Gold Transparency rating for 2023. Charity Navigator, the world's largest evaluator of nonprofits, gave it a score of 83% earning it a rating of three out of four stars, stating, "If this organization aligns with your passions and values, you can give with confidence."

Extraterrestrial liquid water

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