predictions

Testing predictions on supplier governance from the global value chains literature [electronic journal].




predictions

Mutually Consistent Revealed Preference Demand Predictions [electronic journal].




predictions

Ten Clean Energy Stocks: Past Performance and Predictions for 2015

The last two months have not been kind to clean energy stocks. Most commentators attribute the weakness to declining oil prices and the Republicans' strong showing in the midterm elections.




predictions

Removal/emission predictions of wastewater treatment for exposure assessment and Pollutant Release and Transfer Registers

This document compiles information on the current methodologies, tools and models and helps readers identify appropriate models/ and methodologies for estimating substance-specific removal/emissions from wastewater treatment systems. It could support efforts to improve these models and tools.




predictions

Molecular replacement using structure predictions from databases

Molecular replacement (MR) is the predominant route to solution of the phase problem in macromolecular crystallography. Where the lack of a suitable homologue precludes conventional MR, one option is to predict the target structure using bioinformatics. Such modelling, in the absence of homologous templates, is called ab initio or de novo modelling. Recently, the accuracy of such models has improved significantly as a result of the availability, in many cases, of residue-contact predictions derived from evolutionary covariance analysis. Covariance-assisted ab initio models representing structurally uncharacterized Pfam families are now available on a large scale in databases, potentially representing a valuable and easily accessible supplement to the PDB as a source of search models. Here, the unconventional MR pipeline AMPLE is employed to explore the value of structure predictions in the GREMLIN and PconsFam databases. It was tested whether these deposited predictions, processed in various ways, could solve the structures of PDB entries that were subsequently deposited. The results were encouraging: nine of 27 GREMLIN cases were solved, covering target lengths of 109–355 residues and a resolution range of 1.4–2.9 Å, and with target–model shared sequence identity as low as 20%. The cluster-and-truncate approach in AMPLE proved to be essential for most successes. For the overall lower quality structure predictions in the PconsFam database, remodelling with Rosetta within the AMPLE pipeline proved to be the best approach, generating ensemble search models from single-structure deposits. Finally, it is shown that the AMPLE-obtained search models deriving from GREMLIN deposits are of sufficiently high quality to be selected by the sequence-independent MR pipeline SIMBAD. Overall, the results help to point the way towards the optimal use of the expanding databases of ab initio structure predictions.




predictions

Mapping global sea level rise: new gravity data help provide more accurate predictions

Research from the US helps paint a clearer picture of the extent of global sea level rise, by considering new satellite data on the Earth’s gravity. Its findings support reports of accelerating ice melt and suggest that most of the change in sea levels is caused by receding polar ice sheets and mountain glaciers.




predictions

Mobile Video Index to trend in 2018: Openwave mobility predictions

Openwave Mobility released predictions for 2018 based on two major pieces of analysis- the mobile video index (MVI) based on live data gathered from over 30 global mobile operators and the NFV Playbook, based on NFV trials and deployments with insight from leading industry analysts.




predictions

Better predictions of climate change impact on wildlife thanks to genetically informed modelling

The effects of climate change on the distribution of species can be predicted more accurately by considering the genetic differences between different groups of the same species, a new study suggests. The researchers found that a computer model which incorporated genetic information on different groups of a US tree species was up to 12 times more accurate in predicting tree locations than a non-genetically informed model.




predictions

10 worst tech predictions of all time

The technology industry is spurred by those who think ahead and think big — but these predictions were all wrong.



  • Gadgets & Electronics

predictions

Hurricane Irene predictions came true

Sometimes it's not that hard to predict the future and that's just what happened with Hurricane Irene.



  • Climate & Weather

predictions

5 predictions for the green car market

Electric vehicles, hybrids and plug-ins will be on the market by the end of the year. Here's a look into the crystal ball.




predictions

2015 food trend predictions: Ugly, smokey and smelly

But don't be put off by the headline: I'm excited about what I'm hearing about what's coming next year.




predictions

Four politically incorrect predictions about Hurricane Irene

When crisis hits, the jokesters come out of the woodwork. Here is what to expect.




predictions

Flying Stars Fengshui for 2006 : Yearly Predictions & Remedies for Main Door facing South West

If the Main Door of your flat/office/building faces South West, then read on to find out how you will fare in 2006 in your Career/Business, Health, Wealth, Relationships, Harmony and Love.




predictions

Flying Stars Fengshui for 2006 : Yearly Predictions & Remedies for Main Door facing North

If the Main Door of your flat/office/building faces North, then read on to find out how you will fare in 2006 in your Career/Business, Health, Wealth, Relationships, Harmony and Love. Be aware of your strengths and be warned of the negative energy that you may have to face, to plan your strategies well ahead. Also advice regarding directions to be avoided for renovation, reconstruction, redecoration, etc. in 2006. Get to know the Fengshui Enhancers and Cures that can help you, too.




predictions

Flying Stars Fengshui for 2006: Yearly Predictions & Remedies for Main Door facing South

If the Main Door of your flat/office/building faces South, then read on to find out how you will fare in 2006 in your Career/Business, Health, Wealth, Relationships, Harmony and Love. Be aware of your strengths and be warned of the negative energy that you may have to face, to plan your strategies well ahead. Also advice regarding directions to be avoided for renovation, reconstruction, redecoration, etc. in 2006. Get to know the Fengshui Enhancers and Cures that can help you, too.




predictions

Flying Stars Fengshui for 2006: Yearly Predictions & Remedies for Main Door facing East

If the Main Door of your flat/office/building faces East, then read on to find out how you will fare in 2006 in your Career/Business, Health, Wealth, Relationships, Harmony and Love. Be aware of your strengths and be warned of the negative energy that you may have to face, to plan your strategies well ahead. Also advice regarding directions to be avoided for renovation, reconstruction, redecoration, etc. in 2006. Get to know the Fengshui Enhancers and Cures that can help you, too.




predictions

Flying Stars Fengshui for 2006: Yearly Predictions & Remedies for Main Door Facing West

If the Main Door of your flat/office/building faces West, then read on to find out how you will fare in 2006 in your Career/Business, Health, Wealth, Relationships, Harmony and Love. Be aware of your strengths and be warned of the negative energy that you may have to face, to plan your strategies well ahead. Also advice regarding directions to be avoided for renovation, reconstruction, redecoration, etc. in 2006. Get to know the Fengshui Enhancers and Cures that can help you, too.




predictions

Flying Stars Fengshui for 2006 : Yearly Predictions & Remedies for Main Door Facing North East

If the Main Door of your flat/office/building faces North East, then read on to find out how you will fare in 2006 in your Career/Business, Health, Wealth, Relationships, Harmony and Love. Be aware of your strengths and be warned of the negative energy that you may have to face, to plan your strategies well ahead. Also advice regarding directions to be avoided for renovation, reconstruction, redecoration, etc. in 2006. Get to know the Fengshui Enhancers and Cures that can help you, too.




predictions

Flying Stars Fengshui for 2006 : Yearly Predictions & Remedies for Main Door Facing North West

If the Main Door of your flat/office/building faces North West, then read on to find out how you will fare in 2006 in your Career/Business, Health, Wealth, Relationships, Harmony and Love. Be aware of your strengths and be warned of the negative energy that you may have to face, to plan your strategies well ahead. Also advice regarding directions to be avoided for renovation, reconstruction, redecoration, etc. in 2006. Get to know the Fengshui Enhancers and Cures that can help you, too.




predictions

Flying Stars Fengshui for 2006 : Yearly Predictions & Remedies for Main Door facing South East

If the Main Door of your flat/office/building faces South East, then read on to find out how you will fare in 2006 in your Career/Business, Health, Wealth, Relationships, Harmony and Love. Be aware of your strengths and be warned of the negative energy that you may have to face, to plan your strategies well ahead. Also advice regarding directions to be avoided for renovation, reconstruction, redecoration, etc. in 2006. Get to know the Fengshui Enhancers and Cures that can help you, too.




predictions

2020 Legal Marketing Trends & Predictions - Consultwebs

Discover legal marketing trends & predictions in SEO, PPC, Web Design, Content, and Social Media that will impact lawyers in 2020, and get actionable tips from Consultwebs to apply in your law firm.




predictions

Ashton Whiteley: Germany Revises Growth Predictions Upwards

Ashton Whiteley: Despite persistent political uncertainty, the German economy looks set to continue its upward trend in 2018.




predictions

2019 Homecoming Dress Purchasing Trends and Predictions: Pulse of Homecoming

At the beginning of a new homecoming season, Occasion Brands, LLC, releases this 2019 issue of Pulse of Homecoming with pre-season predictions of consumer purchasing trends for homecoming 2019 dresses.




predictions

Best Coronavirus Projections, Predictions, Dashboards and Data Resources

Check out this curated collection of coronavirus-related projections, dashboards, visualizations, and data that we have encountered on the internet.




predictions

30 Big Tech Predictions for 2020

Digital transformation has just begun.

Not a single industry is safe from the unstoppable wave of digitization that is sweeping through finance, retail, healthcare, and more.

In 2020, we expect to see even more transformative developments that will change our businesses, careers, and lives.

See the rest of the story at Business Insider

See Also:




predictions

Knowledge management predictions for 2020

As we approach a new year?and a new decade?executives from multiple industry sectors offer predictions on the intertwined areas of CX, information governance and compliance, and automation and AI




predictions

Predictions and algorithmic statistics for infinite sequence. (arXiv:2005.03467v1 [cs.IT])

Consider the following prediction problem. Assume that there is a block box that produces bits according to some unknown computable distribution on the binary tree. We know first $n$ bits $x_1 x_2 ldots x_n$. We want to know the probability of the event that that the next bit is equal to $1$. Solomonoff suggested to use universal semimeasure $m$ for solving this task. He proved that for every computable distribution $P$ and for every $b in {0,1}$ the following holds: $$sum_{n=1}^{infty}sum_{x: l(x)=n} P(x) (P(b | x) - m(b | x))^2 < infty .$$ However, Solomonoff's method has a negative aspect: Hutter and Muchnik proved that there are an universal semimeasure $m$, computable distribution $P$ and a random (in Martin-L{"o}f sense) sequence $x_1 x_2ldots$ such that $lim_{n o infty} P(x_{n+1} | x_1ldots x_n) - m(x_{n+1} | x_1ldots x_n) rightarrow 0$. We suggest a new way for prediction. For every finite string $x$ we predict the new bit according to the best (in some sence) distribution for $x$. We prove the similar result as Solomonoff theorem for our way of prediction. Also we show that our method of prediction has no that negative aspect as Solomonoff's method.




predictions

Predictions

Copyright 2020 NPR. To see more, visit https://www.npr.org.




predictions

Predictions

Copyright 2020 NPR. To see more, visit https://www.npr.org.




predictions

Cinema Chat: Final Oscar Predictions, 'Three Christs,' 'Birds Of Prey,' And More

There's only a few days left until this year's Oscars are handed out, so now's a good time to catch up on your film viewing. In this week's "Cinema Chat," WEMU's David Fair talks to Michigan and State Theater executive director Russ Collins about the latest movie news and all of the new flicks landing on the big screen this weekend.




predictions

Future Babble: Why Expert Predictions Fail - And Why We Believe Them Anyway by Dan Gardner

Rob Minshull produces Weekends with Warren and is an avid reader. You can hear Dan Gardner being interviewed by Warren Boland on Sunday 13th Weekends with Warren.




predictions

Prescient Predictions: 1984; Brave New World; and Network

The dystopian best-seller 1984 was published exactly seventy years ago. Its influence has been profound. But does it really speak to today’s politico-cultural environment?




predictions

Prescient Predictions: 1984; Brave New World; and Network

The dystopian best-seller 1984 was published exactly seventy years ago. Its influence has been profound. But does it really speak to today’s politico-cultural environment?




predictions

Op-Ed: Predictions about where the coronavirus pandemic is going vary widely. Can models be trusted?

A model predicts COVID-19 deaths in the U.S. will drop to zero by June. Another suggests without a vaccine, the coronavirus will be with us for years.




predictions

When can we travel again? Experts share their predictions

As U.S. faces its most trying coronavirus pandemic days, industry leaders imagine the future of travel.




predictions

2020 Jets schedule: game-by-game predictions

With Tom Brady out of the picture, how will the Jets fare this season?




predictions

2020 Jets schedule: game-by-game predictions

With Tom Brady out of the picture, how will the Jets fare this season?




predictions

'1917' dominates our 2020 Oscar predictions, but 'Parasite' could surprise

Predicting the four acting races for the 2020 Oscars is easy this year, but there's still drama in the best picture race and others




predictions

Nostradamus 2020: Three predictions that came true - is coronavirus the fourth?



NOSTRADAMUS is hailed as the world's greatest prophetic mind, with three predictions people think he got right. Did he predict the coronavirus outbreak?




predictions

30 Big Tech Predictions for 2020

Digital transformation has just begun.

Not a single industry is safe from the unstoppable wave of digitization that is sweeping through finance, retail, healthcare, and more.

In 2020, we expect to see even more transformative developments that will change our businesses, careers, and lives.

To help you stay ahead of the curve, Business Insider Intelligence has put together a list of 30 Big Tech Predictions for 2020 across Banking, Connectivity & Tech, Digital Media, Payments & Commerce, Fintech, and Digital Health.

This exclusive report can be yours for FREE today.

Join the conversation about this story »




predictions

Predictions Review: Trump, Zuck Crush My Optimism In 2019

This past year, I predicted the fall of both Zuck and Trump, not to mention the triumph of cannabis and rationale markets. But in 2019, the sociopaths won – bigly. Damn, was I wrong. One year ago this week, I sat down to write my annual list of ten or so predictions for the coming … Continue reading "Predictions Review: Trump, Zuck Crush My Optimism In 2019"




predictions

Predictions 2020: Facebook Caves, Google Zags, Netflix Sells Out, and Data Policy Gets Sexy

A new year brings another run at my annual predictions: For 17 years now, I’ve taken a few hours to imagine what might happen over the course of the coming twelve months. And my goodness did I swing for the fences last year — and I pretty much whiffed. Batting .300 is great in the majors, but it … Continue reading "Predictions 2020: Facebook Caves, Google Zags, Netflix Sells Out, and Data Policy Gets Sexy"




predictions

Predictions and Policymaking: Complex Modelling Beyond COVID-19

1 April 2020

Yasmin Afina

Research Assistant, International Security Programme

Calum Inverarity

Research Analyst and Coordinator, International Security Programme
The COVID-19 pandemic has highlighted the potential of complex systems modelling for policymaking but it is crucial to also understand its limitations.

GettyImages-1208425931.jpg

A member of the media wearing a protective face mask works in Downing Street where Britain's Prime Minister Boris Johnson is self-isolating in central London, 27 March 2020. Photo by TOLGA AKMEN/AFP via Getty Images.

Complex systems models have played a significant role in informing and shaping the public health measures adopted by governments in the context of the COVID-19 pandemic. For instance, modelling carried out by a team at Imperial College London is widely reported to have driven the approach in the UK from a strategy of mitigation to one of suppression.

Complex systems modelling will increasingly feed into policymaking by predicting a range of potential correlations, results and outcomes based on a set of parameters, assumptions, data and pre-defined interactions. It is already instrumental in developing risk mitigation and resilience measures to address and prepare for existential crises such as pandemics, prospects of a nuclear war, as well as climate change.

The human factor

In the end, model-driven approaches must stand up to the test of real-life data. Modelling for policymaking must take into account a number of caveats and limitations. Models are developed to help answer specific questions, and their predictions will depend on the hypotheses and definitions set by the modellers, which are subject to their individual and collective biases and assumptions. For instance, the models developed by Imperial College came with the caveated assumption that a policy of social distancing for people over 70 will have a 75 per cent compliance rate. This assumption is based on the modellers’ own perceptions of demographics and society, and may not reflect all societal factors that could impact this compliance rate in real life, such as gender, age, ethnicity, genetic diversity, economic stability, as well as access to food, supplies and healthcare. This is why modelling benefits from a cognitively diverse team who bring a wide range of knowledge and understanding to the early creation of a model.

The potential of artificial intelligence

Machine learning, or artificial intelligence (AI), has the potential to advance the capacity and accuracy of modelling techniques by identifying new patterns and interactions, and overcoming some of the limitations resulting from human assumptions and bias. Yet, increasing reliance on these techniques raises the issue of explainability. Policymakers need to be fully aware and understand the model, assumptions and input data behind any predictions and must be able to communicate this aspect of modelling in order to uphold democratic accountability and transparency in public decision-making.

In addition, models using machine learning techniques require extensive amounts of data, which must also be of high quality and as free from bias as possible to ensure accuracy and address the issues at stake. Although technology may be used in the process (i.e. automated extraction and processing of information with big data), data is ultimately created, collected, aggregated and analysed by and for human users. Datasets will reflect the individual and collective biases and assumptions of those creating, collecting, processing and analysing this data. Algorithmic bias is inevitable, and it is essential that policy- and decision-makers are fully aware of how reliable the systems are, as well as their potential social implications.

The age of distrust

Increasing use of emerging technologies for data- and evidence-based policymaking is taking place, paradoxically, in an era of growing mistrust towards expertise and experts, as infamously surmised by Michael Gove. Policymakers and subject-matter experts have faced increased public scrutiny of their findings and the resultant policies that they have been used to justify.

This distrust and scepticism within public discourse has only been fuelled by an ever-increasing availability of diffuse sources of information, not all of which are verifiable and robust. This has caused tension between experts, policymakers and public, which has led to conflicts and uncertainty over what data and predictions can be trusted, and to what degree. This dynamic is exacerbated when considering that certain individuals may purposefully misappropriate, or simply misinterpret, data to support their argument or policies. Politicians are presently considered the least trusted professionals by the UK public, highlighting the importance of better and more effective communication between the scientific community, policymakers and the populations affected by policy decisions.

Acknowledging limitations

While measures can and should be built in to improve the transparency and robustness of scientific models in order to counteract these common criticisms, it is important to acknowledge that there are limitations to the steps that can be taken. This is particularly the case when dealing with predictions of future events, which inherently involve degrees of uncertainty that cannot be fully accounted for by human or machine. As a result, if not carefully considered and communicated, the increased use of complex modelling in policymaking holds the potential to undermine and obfuscate the policymaking process, which may contribute towards significant mistakes being made, increased uncertainty, lack of trust in the models and in the political process and further disaffection of citizens.

The potential contribution of complexity modelling to the work of policymakers is undeniable. However, it is imperative to appreciate the inner workings and limitations of these models, such as the biases that underpin their functioning and the uncertainties that they will not be fully capable of accounting for, in spite of their immense power. They must be tested against the data, again and again, as new information becomes available or there is a risk of scientific models becoming embroiled in partisan politicization and potentially weaponized for political purposes. It is therefore important not to consider these models as oracles, but instead as one of many contributions to the process of policymaking.




predictions

Predictions and Policymaking: Complex Modelling Beyond COVID-19

1 April 2020

Yasmin Afina

Research Assistant, International Security Programme

Calum Inverarity

Research Analyst and Coordinator, International Security Programme
The COVID-19 pandemic has highlighted the potential of complex systems modelling for policymaking but it is crucial to also understand its limitations.

GettyImages-1208425931.jpg

A member of the media wearing a protective face mask works in Downing Street where Britain's Prime Minister Boris Johnson is self-isolating in central London, 27 March 2020. Photo by TOLGA AKMEN/AFP via Getty Images.

Complex systems models have played a significant role in informing and shaping the public health measures adopted by governments in the context of the COVID-19 pandemic. For instance, modelling carried out by a team at Imperial College London is widely reported to have driven the approach in the UK from a strategy of mitigation to one of suppression.

Complex systems modelling will increasingly feed into policymaking by predicting a range of potential correlations, results and outcomes based on a set of parameters, assumptions, data and pre-defined interactions. It is already instrumental in developing risk mitigation and resilience measures to address and prepare for existential crises such as pandemics, prospects of a nuclear war, as well as climate change.

The human factor

In the end, model-driven approaches must stand up to the test of real-life data. Modelling for policymaking must take into account a number of caveats and limitations. Models are developed to help answer specific questions, and their predictions will depend on the hypotheses and definitions set by the modellers, which are subject to their individual and collective biases and assumptions. For instance, the models developed by Imperial College came with the caveated assumption that a policy of social distancing for people over 70 will have a 75 per cent compliance rate. This assumption is based on the modellers’ own perceptions of demographics and society, and may not reflect all societal factors that could impact this compliance rate in real life, such as gender, age, ethnicity, genetic diversity, economic stability, as well as access to food, supplies and healthcare. This is why modelling benefits from a cognitively diverse team who bring a wide range of knowledge and understanding to the early creation of a model.

The potential of artificial intelligence

Machine learning, or artificial intelligence (AI), has the potential to advance the capacity and accuracy of modelling techniques by identifying new patterns and interactions, and overcoming some of the limitations resulting from human assumptions and bias. Yet, increasing reliance on these techniques raises the issue of explainability. Policymakers need to be fully aware and understand the model, assumptions and input data behind any predictions and must be able to communicate this aspect of modelling in order to uphold democratic accountability and transparency in public decision-making.

In addition, models using machine learning techniques require extensive amounts of data, which must also be of high quality and as free from bias as possible to ensure accuracy and address the issues at stake. Although technology may be used in the process (i.e. automated extraction and processing of information with big data), data is ultimately created, collected, aggregated and analysed by and for human users. Datasets will reflect the individual and collective biases and assumptions of those creating, collecting, processing and analysing this data. Algorithmic bias is inevitable, and it is essential that policy- and decision-makers are fully aware of how reliable the systems are, as well as their potential social implications.

The age of distrust

Increasing use of emerging technologies for data- and evidence-based policymaking is taking place, paradoxically, in an era of growing mistrust towards expertise and experts, as infamously surmised by Michael Gove. Policymakers and subject-matter experts have faced increased public scrutiny of their findings and the resultant policies that they have been used to justify.

This distrust and scepticism within public discourse has only been fuelled by an ever-increasing availability of diffuse sources of information, not all of which are verifiable and robust. This has caused tension between experts, policymakers and public, which has led to conflicts and uncertainty over what data and predictions can be trusted, and to what degree. This dynamic is exacerbated when considering that certain individuals may purposefully misappropriate, or simply misinterpret, data to support their argument or policies. Politicians are presently considered the least trusted professionals by the UK public, highlighting the importance of better and more effective communication between the scientific community, policymakers and the populations affected by policy decisions.

Acknowledging limitations

While measures can and should be built in to improve the transparency and robustness of scientific models in order to counteract these common criticisms, it is important to acknowledge that there are limitations to the steps that can be taken. This is particularly the case when dealing with predictions of future events, which inherently involve degrees of uncertainty that cannot be fully accounted for by human or machine. As a result, if not carefully considered and communicated, the increased use of complex modelling in policymaking holds the potential to undermine and obfuscate the policymaking process, which may contribute towards significant mistakes being made, increased uncertainty, lack of trust in the models and in the political process and further disaffection of citizens.

The potential contribution of complexity modelling to the work of policymakers is undeniable. However, it is imperative to appreciate the inner workings and limitations of these models, such as the biases that underpin their functioning and the uncertainties that they will not be fully capable of accounting for, in spite of their immense power. They must be tested against the data, again and again, as new information becomes available or there is a risk of scientific models becoming embroiled in partisan politicization and potentially weaponized for political purposes. It is therefore important not to consider these models as oracles, but instead as one of many contributions to the process of policymaking.




predictions

Mass Spectrometry Based Immunopeptidomics Leads to Robust Predictions of Phosphorylated HLA Class I Ligands [Technological Innovation and Resources]

The presentation of peptides on class I human leukocyte antigen (HLA-I) molecules plays a central role in immune recognition of infected or malignant cells. In cancer, non-self HLA-I ligands can arise from many different alterations, including non-synonymous mutations, gene fusion, cancer-specific alternative mRNA splicing or aberrant post-translational modifications. Identifying HLA-I ligands remains a challenging task that requires either heavy experimental work for in vivo identification or optimized bioinformatics tools for accurate predictions. To date, no HLA-I ligand predictor includes post-translational modifications. To fill this gap, we curated phosphorylated HLA-I ligands from several immunopeptidomics studies (including six newly measured samples) covering 72 HLA-I alleles and retrieved a total of 2,066 unique phosphorylated peptides. We then expanded our motif deconvolution tool to identify precise binding motifs of phosphorylated HLA-I ligands. Our results reveal a clear enrichment of phosphorylated peptides among HLA-C ligands and demonstrate a prevalent role of both HLA-I motifs and kinase motifs on the presentation of phosphorylated peptides. These data further enabled us to develop and validate the first predictor of interactions between HLA-I molecules and phosphorylated peptides.




predictions

Predictions and Policymaking: Complex Modelling Beyond COVID-19

1 April 2020

Yasmin Afina

Research Assistant, International Security Programme

Calum Inverarity

Research Analyst and Coordinator, International Security Programme
The COVID-19 pandemic has highlighted the potential of complex systems modelling for policymaking but it is crucial to also understand its limitations.

GettyImages-1208425931.jpg

A member of the media wearing a protective face mask works in Downing Street where Britain's Prime Minister Boris Johnson is self-isolating in central London, 27 March 2020. Photo by TOLGA AKMEN/AFP via Getty Images.

Complex systems models have played a significant role in informing and shaping the public health measures adopted by governments in the context of the COVID-19 pandemic. For instance, modelling carried out by a team at Imperial College London is widely reported to have driven the approach in the UK from a strategy of mitigation to one of suppression.

Complex systems modelling will increasingly feed into policymaking by predicting a range of potential correlations, results and outcomes based on a set of parameters, assumptions, data and pre-defined interactions. It is already instrumental in developing risk mitigation and resilience measures to address and prepare for existential crises such as pandemics, prospects of a nuclear war, as well as climate change.

The human factor

In the end, model-driven approaches must stand up to the test of real-life data. Modelling for policymaking must take into account a number of caveats and limitations. Models are developed to help answer specific questions, and their predictions will depend on the hypotheses and definitions set by the modellers, which are subject to their individual and collective biases and assumptions. For instance, the models developed by Imperial College came with the caveated assumption that a policy of social distancing for people over 70 will have a 75 per cent compliance rate. This assumption is based on the modellers’ own perceptions of demographics and society, and may not reflect all societal factors that could impact this compliance rate in real life, such as gender, age, ethnicity, genetic diversity, economic stability, as well as access to food, supplies and healthcare. This is why modelling benefits from a cognitively diverse team who bring a wide range of knowledge and understanding to the early creation of a model.

The potential of artificial intelligence

Machine learning, or artificial intelligence (AI), has the potential to advance the capacity and accuracy of modelling techniques by identifying new patterns and interactions, and overcoming some of the limitations resulting from human assumptions and bias. Yet, increasing reliance on these techniques raises the issue of explainability. Policymakers need to be fully aware and understand the model, assumptions and input data behind any predictions and must be able to communicate this aspect of modelling in order to uphold democratic accountability and transparency in public decision-making.

In addition, models using machine learning techniques require extensive amounts of data, which must also be of high quality and as free from bias as possible to ensure accuracy and address the issues at stake. Although technology may be used in the process (i.e. automated extraction and processing of information with big data), data is ultimately created, collected, aggregated and analysed by and for human users. Datasets will reflect the individual and collective biases and assumptions of those creating, collecting, processing and analysing this data. Algorithmic bias is inevitable, and it is essential that policy- and decision-makers are fully aware of how reliable the systems are, as well as their potential social implications.

The age of distrust

Increasing use of emerging technologies for data- and evidence-based policymaking is taking place, paradoxically, in an era of growing mistrust towards expertise and experts, as infamously surmised by Michael Gove. Policymakers and subject-matter experts have faced increased public scrutiny of their findings and the resultant policies that they have been used to justify.

This distrust and scepticism within public discourse has only been fuelled by an ever-increasing availability of diffuse sources of information, not all of which are verifiable and robust. This has caused tension between experts, policymakers and public, which has led to conflicts and uncertainty over what data and predictions can be trusted, and to what degree. This dynamic is exacerbated when considering that certain individuals may purposefully misappropriate, or simply misinterpret, data to support their argument or policies. Politicians are presently considered the least trusted professionals by the UK public, highlighting the importance of better and more effective communication between the scientific community, policymakers and the populations affected by policy decisions.

Acknowledging limitations

While measures can and should be built in to improve the transparency and robustness of scientific models in order to counteract these common criticisms, it is important to acknowledge that there are limitations to the steps that can be taken. This is particularly the case when dealing with predictions of future events, which inherently involve degrees of uncertainty that cannot be fully accounted for by human or machine. As a result, if not carefully considered and communicated, the increased use of complex modelling in policymaking holds the potential to undermine and obfuscate the policymaking process, which may contribute towards significant mistakes being made, increased uncertainty, lack of trust in the models and in the political process and further disaffection of citizens.

The potential contribution of complexity modelling to the work of policymakers is undeniable. However, it is imperative to appreciate the inner workings and limitations of these models, such as the biases that underpin their functioning and the uncertainties that they will not be fully capable of accounting for, in spite of their immense power. They must be tested against the data, again and again, as new information becomes available or there is a risk of scientific models becoming embroiled in partisan politicization and potentially weaponized for political purposes. It is therefore important not to consider these models as oracles, but instead as one of many contributions to the process of policymaking.




predictions

[Accounts of medical and magical character, fortune tellings and predictions]

19th century.




predictions

Gaming in 2020: 4 Reasonable Predictions and 2 Ridiculous Ones

2019 is nearly over, so let's look ahead to what awaits the video game industry in the first year of the new decade. Informed opinions and hot takes abound.




predictions

The 2025 User Experience: Predictions for the Future of Personalized Technology

History is witness to the many scientific leaders and technology visionaries who all tried to predict what innovations will exist in the future. While not all predictions come to fruition, others were not so far off. We may not have flying cars like The...