Trend Extrapolations Research Articles

Acceleration neglect in trend extrapolations.

Many decisions rely on intuitive predictions based on time series data showing a trend. For instance, the current upward trend in global temperatures might lead to specific predictions about the extent to which global temperatures will rise in the future, and these predictions might be used to inform judgments about the urgency with which climate change must be addressed. However, those predictions often need to be revised to incorporate the effects of unexpected events that might accelerate a trend (i.e., increase its rate of change), such as an unanticipated increase in CO₂ emissions, or decelerate a trend (i.e., decrease its rate of change), such as an unanticipated reduction in CO₂ emissions. In this work, we uncover a new cognitive bias by which people neglect how much a trend can accelerate (vs. decelerate) due to unexpected events. We explain this bias in terms of momentum theory and a naive understanding of physics. These findings have important implications for businesses and policymakers seeking to communicate information about topics such as climate change, stock market prices, or disease prevention. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Supplemental Material for Acceleration Neglect in Trend Extrapolations

Supplemental Material for Acceleration Neglect in Trend Extrapolations

10.F. Workshop: Projecting future burden of disease for informed public health decision making

Abstract What are the most relevant diseases in a country? As the need for prioritising the use of available resources constantly increases, a timely, sound and comprehensive answer to this question is more than ever needed to inform public health decision making. The burden of disease metrics such as the Disability-adjusted Life Years (DALY) are important indicators for health information. The DALY provides a comprehensive indicator of the health gap in a country, addressing both premature mortality and health loss due to the occurrence of diseases. The European Burden of Disease Network (COST Action CA18218) acts as a technical platform for integrating and strengthening capacity in burden of disease assessment across Europe and beyond. The Action places a key focus on identifying and addressing data challenges, methodological choices and the need for translating research into policy. Where many Burden of disease (BoD) studies aim at producing the best estimates for the recent years, some countries are also making future projections of DALYs. By doing so, BoD studies become even more relevant for the policy making process since the do not cover only current best estimates, but also show what policy makers might be anticipating on. There are different methods to make future projections, varying from trend extrapolations and forecasts to developing scenarios as part of and advanced foresight approach. Future projections of the DALY estimates bring along additional data requirements. To be able to calculate future DALYs, projections of prevalence and causes of death, but also of population demographics, are needed. In addition, methodological choices have to be made to calculate future DALYs, for example regarding life tables. In this workshop, all these aspects will be addressed. Several countries (i.e. The Netherlands, Belgium, Scotland and Norway) will present their approaches to estimate future DALYs. The workshop will start with and interactive Mentimeter session to get a better view on how the audience is familiar with (future) Burden of Disease. Then, a brief presentation is given as introduction to the European Burden of Disease Network. This is followed by the four presentations of future DALYs. The workshop will be concluded by a discussion with the presenters on the lessons learned regarding their experience of doing future projections. Key messages • Burden of disease becomes even more relevant for policy makers when projections are included in the Burden of Disease study. • Data and methodological issues should be properly addressed to better inform policy makers.

Footprints to singularity: A global population model explains late 20th century slow-down and predicts peak within ten years.

Projections of future global human population are traditionally made using birth/death trend extrapolations, but these methods ignore limits. Expressing humanity as a K-selected species whose numbers are limited by the global carrying capacity produces a different outlook. Population data for the second millennium up to the year 1970 was fit to a hyper-exponential growth equation, where the rate constant for growth itself grows exponentially due to growth of life-saving technology. The discrepancies between the projected growth and the actual population data since 1970 are accounted for by a decrease in the global carrying capacity due to ecosystem degradation. A system dynamics model that best fits recent population numbers suggests that the global biocapacity may already have been reduced to one-half of its historical value and global carrying capacity may be at its 1965 level and falling. Simulations suggest that population may soon peak or may have already peaked. Population projections depend strongly on the unknown fragility or robustness of the Earth's essential ecosystem services that affect agricultural production. Numbers for the 2020 global census were not available for this study.

A comprehensive set of global scenarios of housing, mobility, and material efficiency for material cycles and energy systems modeling

Scenario-based assessments are a useful tool to explore unknown futures and inform decision makers and the public of the consequences of different courses of action. Scenario developments in industrial ecology have focused on disparate components of the socioeconomic metabolism and case studies, and few efforts of comprehensive and cumulative scenario formulation are documented. Many important, empirically derived relationships between material cycles, end-use services, and energy use are relevant to global scenario modeling efforts, for example, of integrated assessment models (IAMs), which do not routinely describe material cycles or the life-cycle impacts of various technology shifts. These inconsistent depictions of material cycles and their environmental impacts hinder the assessment of sustainable development strategies such as demand-side sufficiency, material efficiency, and energy efficiency. We developed three highly detailed scenarios covering 20 global regions to 2060 for the service provisioning of dwelling area and personal transport grounded in salient building and vehicle operation parameters. Our scenarios are based on, and interface with, the Low Energy Demand (LED) and Shared Socioeconomic Pathways (SSP1 and SSP2) narratives. The results comprise scenario-, region-, and period-specific narratives and corresponding parameter values, including per-capita floor space and vehicle stocks, building and vehicle archetype mixes, passenger-km, vehicle-km, vehicle occupancy rates, and implementation potentials of nine material efficiency strategies. The explicit storyline extension approach presented here is an alternative to the aggregate GDP-driven or historical trend extrapolations of service or energy demands. We describe the scenario formulation processes, resulting parameters, their applications, and offer an outlook for prospective sustainability models. This article met the requirements for a Gold-Gold JIE data openness badge described at http://jie.click/badges.

Estimating Demand for Industrial and Commercial Land Use Given Economic Forecasts

Current developments in the field of land use modelling point towards greater level of spatial and thematic resolution and the possibility to model large geographical extents. Improvements are taking place as computational capabilities increase and socioeconomic and environmental data are produced with sufficient detail. Integrated approaches to land use modelling rely on the development of interfaces with specialized models from fields like economy, hydrology, and agriculture. Impact assessment of scenarios/policies at various geographical scales can particularly benefit from these advances. A comprehensive land use modelling framework includes necessarily both the estimation of the quantity and the spatial allocation of land uses within a given timeframe. In this paper, we seek to establish straightforward methods to estimate demand for industrial and commercial land uses that can be used in the context of land use modelling, in particular for applications at continental scale, where the unavailability of data is often a major constraint. We propose a set of approaches based on ‘land use intensity’ measures indicating the amount of economic output per existing areal unit of land use. A base model was designed to estimate land demand based on regional-specific land use intensities; in addition, variants accounting for sectoral differences in land use intensity were introduced. A validation was carried out for a set of European countries by estimating land use for 2006 and comparing it to observations. The models’ results were compared with estimations generated using the ‘null model’ (no land use change) and simple trend extrapolations. Results indicate that the proposed approaches clearly outperformed the ‘null model’, but did not consistently outperform the linear extrapolation. An uncertainty analysis further revealed that the models’ performances are particularly sensitive to the quality of the input land use data. In addition, unknown future trends of regional land use intensity widen considerably the uncertainty bands of the predictions.

Bibliometric data and actual development in technology life cycles: flaws in assumptions

Purpose – Motivated with the ever growing number of bibliometric trend extrapolation studies, the purpose of this paper is to demonstrate through two technologies how the selection of an upper limit of growth affects the correlation and causality of technology development measured with bibliometric data. Design/methodology/approach – The paper uses Gompertz and Fisher-Pry curves to model the technological development of white light emitting diodes and flash memory, and show with extrapolation results from several bibliometric sources how a typical bias is caused in trend extrapolations. Findings – The paper shows how drastic an effect the decision to set an upper bound has on trend extrapolations, to be used as a reference for applications. The paper recommends carefully examining the interconnection of actual development and bibliometric activity. Originality/value – Despite increasing interest in modelling technological data using this method, reports rarely discuss basic assumptions and their effects on outcomes. Since trend extrapolations are applied more widely in different disciplines, the basic limitations of methods should be explicitly expressed.

Research Report—Principles for Examining Predictive Validity: The Case of Information Systems Spending Forecasts

Research over two decades has advanced the knowledge of how to assess predictive validity. We believe this has value to information systems (IS) researchers. To demonstrate, we used a widely cited study of IS spending. In that study, price-adjusted diffusion models were proposed to explain and to forecast aggregate U.S. information systems spending. That study concluded that such models would produce more accurate forecasts than would simple linear trend extrapolation. However, one can argue that the validation procedure provided an advantage to the diffusion models. We reexamined the results using an alternative validation procedure based on three principles extracted from forecasting research: (1) use ex ante (out-of-sample) performance rather than the fit to the historical data, (2) use well-accepted models as a basis for comparison, and (3) use an adequate sample of forecasts. Validation using this alternative procedure did confirm the importance of the price-adjustment, but simple trend extrapolations were found to be more accurate than the price-adjusted diffusion models.

Research Report—Modeling vs. Forecasting: The Case of Information Systems Spending

Collopy, Adya and Armstrong (1994) (CAA) advocate the use of atheoretical “black box” extrapolation techniques to forecast information systems spending. In this paper, we contrast this approach with the positive modeling approach of Gurbaxani and Mendelson (1990), where the primary focus is on explanation based on economics and innovation diffusion theory. We argue that the objectives and premises of extrapolation techniques are so fundamentally different from those of positive modeling that the evaluation of positive models using the criteria of “black box” forecasting approaches is inadequate. We further show that even if one were to accept CAA's premises, their results are still inferior. Our results refute CAA's claim that linear trend extrapolations are appropriate for forecasting future IS spending and demonstrate the risks of ignoring the guidance of theory.

Whole-pattern futures projection, using field anomaly relaxation

The field anomaly relaxation (FAR) method for projecting whole-pattern future scenarios was designed with reference to the following postulates. 1. 1. Most policies will not come to fruition until years after they have been chosen, so evaluation of options should be done with an eye toward future contexts rather than present ones. The future cannot be reliably predicted; for that reason as well as for other comparably cogent ones, logical derivation of the best policy is infeasible. Therefore, judgment (the alternative to strictly logical analysis) must be used in final policy-issue resolution. 2. 2. Still, analytical exploration of the issue faced by a policy chooser can be an extremely useful heuristic aid to judgment. Judgments also are needed within each such study, however, and they should be made against the same future contexts to be used in terminally resolving the issue in question. 3. 3. Judgment is basically the same as intuition. As such, it is an event rather than a process—a sudden feeling that one option fits best within the judge's inner, gestalt appreciation of the infinitely fine-grained web of circumstance wherein the chosen alternative will have to “live” in the future. Future projections to be used as contexts for policy-related judgments therefore should describe whole, coherent, quasi-organic patterns of life in the field (in the Lewinian sense) of concern. Full and explicit description of such a field is impossible, so skeletal descriptions must be sought instead. Field theory points out some of the criteria to be met; other criteria derive from the importance of engendering similar gestalt appreciations of the described pattern in a number of different minds. The FAR method meets an extended list of such criteria, because it was designed to do so; we at PSI have been able to find no other method that meets all of them, or was ever expected to do so. FAR combines Lewin's social field theory, Zwicky's morphological approaches, and the relaxation methods of engineering. It does not attempt to derive the most probable future line of evolution within the social field addressed, nor even the most plausible. Neither does it follow deductive lines of analysis by trying to trace the consequences of competing assumptions for each of the several (in particular, seven) different aspects of that field that should be considered, since the uncertainties introduced in sucha dendritic approach cascade unmanageably. Rather, FAR brings component bits of information and insight together by focusing on the matter of internal consistency within putatively plausible patterns and sequences of patterns. It yields a set of comparably plausible scenarios for a chosen field, each descriptive of changing circumstances (rather than events) over a future span of one to three decades; the set rarely need number more than a dozen, in order to satisfactorily cover the perceived range of plausible change. It uses trend extrapolations only tangentially, as often to help expose implausibilities as to forecast the future. Its iterative approach is ordered, but mostly qualitative, and it is insistently holistic rather than reductionist in spirit. Part I of this paper elaborates the preceding argument and describes the FAR method in detail. Part II describes some of FAR's applications during the decade since it took form, together with some of the lessons learned from that work. It ends by inquiring into problems of effecting institutional applications of the new method, and into some potential extensions and refinements of it.

Rent Control: Concepts, Realities, and Mechanisms, by Monica R. Lett

New Jersey toward the Year 2000 converts a series of assumptions about births, deaths, migration, jobs, unemployment, and other socioeconomic indicators into population and employment projections for New Jersey's counties and municipalities. Employment projections for some counties in the state are produced by regional agencies, however, not all of the state's counties are covered by these agencies; and for those that are covered, the projections are not necessarily consistent. The authors argue that the differences among techniques available for employment projection can be understood by partitioning them into three broad categories: trend extrapolations that are statistical projections of employment as a function of time; market share models that project change in one geographic area as a function of projected changes in another market area of which the former is a part; and models of sectoral interdependence that commonly see changes in the exporting sectors of the economy as having a multiplier effect on the non-exporting sector of the economy. Connie O. Michaelson and Michael R. Greenberg have gathered over 12,000 employment projections for the state of New Jersey and its twenty-one counties. Specifically, 168 projections are offered for 23 industrial sectors for the state and each county. Since most volumes of this sort offer fewer projections, a summary of the employment series is offered here. This overview makes clear the kinds of uses that the reader may make of the series projections. The final chapter breaks down the authors' research by county and includes graphical representations.

Methods in futures studies: A view from the theory of science

Viewing forecasting in terms of three modes, as predictions deduced from tested scientific theories, as trend extrapolations, and as heuristics, it is shown that most long-term predictions are inherently contingent. They do not tell us what will happen unconditionally, but what will happen, in case something else happens. Hence futures studies will not show us any predetermined future, but rather different ways of shaping the future. Scientific predictions, trend extrapolation, and heuristic methods are the most important approaches to forecasting. If they are properly understood, this will help understanding other less important methods also.

Interregional forecast of student enrollment ? A simulation approach

It is essential for any planning of educational activities to have some estimate of future student numbers. Therefore, the interest in forecasting studies of student enrollments is widespread and the number of them is legion. However, a survey of the available literature gives the impression that most of the researchers spend a large amount of the time fighting the inadequate data base and do not indulge themselves in highly sophisticated techniques. The reason given is that complex forecasting models cannot be justified by the weal; data base; which is an error since neither very simple nor highly sophisticated procedures can alter data limitations. However, the latter type of methods offers the possibility to work out alternatives to project enrollment figures based on different assumptions, to exhibit missing data and to serve as a framework for further research. After giving a brief introduction into the literature on forecasting of educational enrollment, a model will be presented which is based on the flows of students between regions and institutions, together with some examples of usage of the basic flow matrix. There are two types of projection studies. The first group, basically trend extrapolations of historical data, will be called "demographic forecast" for simplicity, whereas the second group, here called "educational models, " includes socio-economic variables and usually uses complex mathematical models (Peucker, T. K. 1968).