Economic Forecasting with an Agent-Based Model

Poledna, Sebastian; Miess, Michael; Hommes, Cars

doi:10.2139/ssrn.3484768

Cited by 35 publications

(29 citation statements)

References 65 publications

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“…In most OECD countries, the large majority of younger adults (age [25][26][27][28][29][30][31][32][33][34] had at least an upper secondary qualification in 2017. The proportion of [25][26][27][28][29][30][31][32][33][34] year olds with tertiary education also increased, from 34% in 2007 to 44% in 2017 on average across the OECD. In just a few decades, upper secondary schooling has been transformed from a vehicle for upward social mobility into a minimum requirement for life in modern society (OECD, 2018a).…”

Section: Trends In Education and Ageingmentioning

confidence: 99%

“…Population with tertiary education (2018 or latest available)[25][26][27][28][29][30][31][32][33][34] year-olds / 55-64 year-olds, percentage in same age group Note: In most countries, there is a break in the time series as data for 2017 refer to ISCED 2011, while data for 2007 refer to ISCED 97 Source: OECD (2019), Population with tertiary education (indicator). https://doi.org/10.1787/0b8f90e9-en…”

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confidence: 99%

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Outlook: Towards an OECD-IIASA Multiannual Work Programme

2020

New Approaches to Economic Challenges

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Photo credits: Cover PrefaceIn policymaking, we hear the word "system" all the time. The economic system. The education system. The financial system. The political system. The social system… However, we rarely hear the word system attached to the word "approach". But unless we adopt a systems approach, unless we employ systems thinking, we will fail to understand the world we are living in. Our world is made up of complex systems, systems of systems interacting with each other, and changing each other by that interaction and the links between them.The global economy now has a greater number of links than ever before. The global nature of supply chains; new ways of exchanging goods, services and ideas; increasing migration; and ever-greater digitalisation, all increase our global connectedness. Such interconnectedness, in turn, gives rise to complexity, and this can be good or bad. However, within mainstream economics, the understanding of why and when interconnectedness may increase stability or instability has remained fragmented.Complexity science helps us to understand the main features of the most important systems we have to deal with. Features such as emergence, when the overall effect of individuals' actions is qualitatively different from what each of the individuals is doing. Radical uncertainty is another important aspect. It describes surprises-outcomes or events that are unanticipated, that cannot be put into a probability distribution because they are outside our list of things that might occur.But despite radical uncertainty and the unanticipated, the future is born by anticipation. We take decisions and perform actions to influence the future, as individuals, societies or governments. And the imagined, probable, or expected outcomes influence our decisions and actions in the present. Even things that may never happen, or will only happen decades from now, can have an impact on what we do today. That is why we plan our day, buy insurance, and pay into pension funds. That is why we try to forecast everything from GDP to the weather to the results of elections or football matches.Of course, not all our decisions about the future are in tune with what economic rationality would like us to do. We promote evidence-based decision-making, but of course there is no evidence about the future. Moreover, experience shows that simply extrapolating from the past can be ridiculous, dangerous or at best misguided.A complexity approach helps us to avoid these errors. We are dealing with a world characterised by nonlinearities, tipping points, and asymmetrical relations where a small cause can have a big effect. In a systems approach, global issues need global solutions. Environmental problems do not respect borders. You need to import some goods and services to be able to export others. The digital revolution is making it hard to define what the "domestic" in "gross domestic product" is. Growing inequalities are creating discontent.If we are to tackle these issues, governments must change the ways in which they des...

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Section: Trends In Education and Ageingmentioning

confidence: 99%

mentioning

confidence: 99%

Outlook: Towards an OECD-IIASA Multiannual Work Programme

2020

New Approaches to Economic Challenges

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“…At the same time, the level of education of the population has been increasing. In most OECD countries, the large majority of younger adults (age [25][26][27][28][29][30][31][32][33][34] had at least an upper secondary qualification in 2017. The proportion of 25-34 year olds with tertiary education also increased, from 34% in 2007 to 44% in 2017 on average across the OECD.…”

Section: Trends In Education and Ageingmentioning

confidence: 99%

“…The digital nature of these platforms often means that employers can find workers worldwide, and the variability in labour standards and living costs across countries can often mean a "race-to-the-bottom" for all workers. Those countries with higher rates of non-standard work also have lower wages, less employment protection, less access to social protection, and low bargaining power (OECD, 2014 [30]).…”

Section: Emerging Linkages Between Technology and Workmentioning

confidence: 99%

Systems-based Approaches for Development Co-operation to Meet Diverse Needs and Aspirations in an Interdependent World

Sperling¹,

Lopes²,

Malhotra³

et al. 2020

New Approaches to Economic Challenges

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In policymaking, we hear the word "system" all the time. The economic system. The education system. The financial system. The political system. The social system… However, we rarely hear the word system attached to the word "approach". But unless we adopt a systems approach, unless we employ systems thinking, we will fail to understand the world we are living in. Our world is made up of complex systems, systems of systems interacting with each other, and changing each other by that interaction and the links between them. The global economy now has a greater number of links than ever before. The global nature of supply chains; new ways of exchanging goods, services and ideas; increasing migration; and ever-greater digitalisation, all increase our global connectedness. Such interconnectedness, in turn, gives rise to complexity, and this can be good or bad. However, within mainstream economics, the understanding of why and when interconnectedness may increase stability or instability has remained fragmented. Complexity science helps us to understand the main features of the most important systems we have to deal with. Features such as emergence, when the overall effect of individuals' actions is qualitatively different from what each of the individuals is doing. Radical uncertainty is another important aspect. It describes surprises-outcomes or events that are unanticipated, that cannot be put into a probability distribution because they are outside our list of things that might occur. But despite radical uncertainty and the unanticipated, the future is born by anticipation. We take decisions and perform actions to influence the future, as individuals, societies or governments. And the imagined, probable, or expected outcomes influence our decisions and actions in the present. Even things that may never happen, or will only happen decades from now, can have an impact on what we do today. That is why we plan our day, buy insurance, and pay into pension funds. That is why we try to forecast everything from GDP to the weather to the results of elections or football matches. Of course, not all our decisions about the future are in tune with what economic rationality would like us to do. We promote evidence-based decision-making, but of course there is no evidence about the future. Moreover, experience shows that simply extrapolating from the past can be ridiculous, dangerous or at best misguided. A complexity approach helps us to avoid these errors. We are dealing with a world characterised by nonlinearities, tipping points, and asymmetrical relations where a small cause can have a big effect. In a systems approach, global issues need global solutions. Environmental problems do not respect borders. You need to import some goods and services to be able to export others. The digital revolution is making it hard to define what the "domestic" in "gross domestic product" is. Growing inequalities are creating discontent. If we are to tackle these issues, governments must change the ways in which they design and implement poli...

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“…Passmore et al [17] indicate the need for the use of formal verification methods in financial practice and regulation; fostering the design, implementation, and regulation of critical algorithms that run modern financial markets. For the sake of completeness, we also report approaches for the identification of economic models through simulation, such as multi-agent resource allocation [21] or agent-based modeling [19].…”

Section: Related Workmentioning

confidence: 99%

Sabrina: Modeling and Visualization of Financial Data over Time with Incremental Domain Knowledge

Arleo

Sorger

Tsigkanos

et al. 2019

2019 IEEE Visualization Conference (VIS)

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Figure 1: The different visualizations composing the Sabrina system. The three views (a, b, c) display the firm density and transaction data of an Austrian economy dataset [3]: (a) gradient encoding in blue/red indicates whether the firm density in a hexagonal area increased/decreased in respect to the previous time step; (b) inferred transactions between individual companies; (c) regionally clustered firm data. The interface allows the user to (d) navigate the temporal data dimension, (e) show aggregated details on selected transaction flows, and (f) adjust encoding and filtering options. Tooltips (g) show details on firms within a bin. ABSTRACTInvestment planning requires knowledge of the financial landscape on a large scale, both in terms of geo-spatial and industry sector distribution. There is plenty of data available, but it is scattered across heterogeneous sources (newspapers, open data, etc.), which makes it difficult for financial analysts to understand the big picture. In this paper, we present Sabrina, a financial data analysis and visualization approach that incorporates a pipeline for the generation of firm-to-firm financial transaction networks. The pipeline is capable of fusing the ground truth on individual firms in a region with (incremental) domain knowledge on general macroscopic aspects of the economy. Sabrina unites these heterogeneous data sources within a uniform visual interface that enables the visual analysis process. In a user study with three domain experts, we illustrate the usefulness of Sabrina, which eases their analysis process.

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Economic Forecasting with an Agent-Based Model

Cited by 35 publications

References 65 publications

Outlook: Towards an OECD-IIASA Multiannual Work Programme

Outlook: Towards an OECD-IIASA Multiannual Work Programme

Systems-based Approaches for Development Co-operation to Meet Diverse Needs and Aspirations in an Interdependent World

Sabrina: Modeling and Visualization of Financial Data over Time with Incremental Domain Knowledge

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