Forecasting innovations in science, technology, and education

Abstract

Human survival depends on our ability to predict future outcomes so that we can make informed decisions. Human cognition and perception are optimized for local, short-term decision-making, such as deciding when to fight or flight, whom to mate, or what to eat. For more elaborate decisions (e.g., when to harvest, when to go to war or not, and whom to marry), people used to consult oracles-prophetic predictions of the future inspired by the gods. Over time, oracles were replaced by models of the structure and dynamics of natural, technological, and social systems. In the 21st century, computational models and visualizations of model results inform much of our decision-making: near real-time weather forecasts help us decide when to take an umbrella, plant, or harvest; where to ground airplanes; or when to evacuate inhabitants in the path of a hurricane, tornado, or flood (1). Long-term weather and climate forecasts predict a future with increasing torrential rains, stronger winds, and more frequent drought, landslides, and forest fires as well as rising sea levels, enabling decision makers to prepare for these changes by building dikes, moving cities and roads, and building larger water reservoirs and better storm sewers (2). Power of Forecasts Computational models are particularly useful if they are combined with high-quality data and if they are widely used and understood. As early as 1960, Buckminster Fuller proposed the "World Game" to address the world's problems through a holistic and anticipatory systems approach (3, 4). The game used Fuller's Dymaxion Map to visualize resources, trends, and scenarios. It was meant to be accessible to everyone (not just experts); therefore, decisions could be made collectively, and results could be used by anyone. In the 1970s, "The limits to growth: A report to the club of Rome" (5) used simulations to forecast future states of the world; a 30-y update was published in 2004 (6). Today, computers and the internet provide the technological infrastructure for universal access to data and compute power. Advances in mathematics, computer science, engineering, and other disciplines have made it possible to implement scalable, empirically validated computational models that render data into actionable insights. Because datasets are huge and multidimensional and models are complex, both tend to exceed human comprehension. Data visualizations and novel interfaces are being developed to help communicate the inner workings of computational models as well as model results to diverse stakeholder groups, including public audiences. For example, the Places & Spaces: Mapping Science exhibit (scimaps. org) features more than 100 large-scale maps of science and 18 interactive data visualizations designed by more than 230 authors. Elsewhere, the Data & Network Science in K-20 Education initiative (www.bu.edu/networks) gives students an entry point to understand and make meaning of science and technology (S&T) network models, data visualizations, and the role of forecasting. Diverse Forecasts f...

Forecasting for intended consequences

“…Simulation (or “computational”) models are powerful, well‐established tools for forecasting and evaluating conservation actions, allowing managers to address some of the barriers to conservation innovation (Brum, Pressey, Bini, & Loyola, 2019; DeAngelis et al, 1998; Larson, Thompson III, Millspaugh, Dijak, & Shifley, 2004; Nilsson et al, 2005; Ravenscroft, Scheller, Mladenoff, & White, 2010; Tulloch, Hagger, & Greenville, 2020; Twilley, Rivera‐Monroy, Chen, & Botero, 1999). Simulation models and their associated visualizations inform many critical policy and decision‐making endeavors today (Börner, Rouse, Trunfio, & Stanley, 2018), including global climate change (Lempert & Groves, 2010; Neilson et al, 2005), pandemic spread and response (Giordano et al, 2020; Hall, Gani, Hughes, & Leach, 2007), transportation planning and operation (Robinson, 2012), homeland security risk assessment (Ezell, 2012), and business process efficiency and performance (Diaz, Behr, & Tulpule, 2012) among others. Forecasting has rapidly developed over the past decade thanks to improvements in software and hardware (Scheller, 2018) and, combined with the rise in availability of large ecological data sets that expand the scales of observation, is being increasingly used to examine pressing ecological problems (Cheruvelil & Soranno, 2018).…”

İqtisadi̇ İnki̇şafin İnklüzi̇vli̇k Xüsusi̇yyətləri̇ Və Onun Formalaşmasi Səvi̇yyəsi̇ni̇n Qi̇ymətləndi̇ri̇lməsi̇ni̇n Metodoloji̇ Əsaslari

“…Dövlət səviyyəsində isə institutsional mexanizmlər, inklüziv normativlər, inklüziv infrastruktur, inklüziv idarəetmə vasitələri işlənilir və qəbul edilir. Bunlar isə fərdi, müəssisə, regional, sahə və ümumdövlət səviyyəsində inklüziv idarəetmə proseslərinə müsbət təsir edir və cəmiyyətin ümumi inklüzivlik səviyyəsini yüksəldir [6].…”

Where is your field going? A machine learning approach to study the relative motion of the domains of physics

“…For instance, Martinez et al investigate the impact on education and labour of technological and scientific progress and on the feedbacks which in turn are given from education and labour market to science and technology [3]. Börner et al, instead, discuss the importance of having reliable predictive models in science, technology and economics paired with an easily readable data visualization procedure to help policy makers in their activity [4]. As we will show in the following, our methodology allows for concrete predictions about the time evolution of scientific fields.…”