Gaming for better decision making – one winner at Climate-Kic Nordic Ideation Day

The project Gaming for better decision making was one of the winners at the Climate-Kic Nordic Ideation Day in Aarhus August 23 2017.

Ullrika was there pitching it in front of a jury. Great day with a lot of positive feedback and useful connections.

Gaming for better decisions under uncertainty is a project with the aim to develop a computer game that motivates people to learn about uncertainty analysis and decision making in an entertaining way. The game will explain the benefits of expressing uncertainty when making predictions and possible ways to make decisions under uncertainty.

The game will include prediction and decision problems from daily life that players can relate to, and a fictional game scenario that involves decision making under climate uncertainty, where there is a need to consider the balance between social, economic, and environmental impacts. A case-study on climate decisions under uncertainty will be developed on flood protection in the municipality of Vejle, Denmark. We will benefit from previous experience in serious gaming at Aarhus University.

By the end of 2017 we will have an operational game prototype.

The game is expected to result in

– a greater interest to learn more about uncertainty among experts and decision makers

– a wider use of subjective probability for uncertainty

– a tool to initiate discussions on how to adapt uncertainty analysis to your decision problem

Partners: Lund University, DTU, Aarhus University, Vejle municipality

People (to be updated):

Ullrika Sahlin, Associate Professor, Lund University, Sweden. Uncertainty analysis in risk and environmental impact assessment.

Anthony O’Hagan Emeritus Professor, the University of Sheffield, UK. Eliciting expert knowledge using subjective probability; e-learning to train experts to express uncertainty accurately and rigorously.

Igor Linkov, Adjunct Professor, Carnegie Mellon University. Expert on decision making with applications on climate adaption and development of serious games of decision making.

Igor Kozine, Senior Researcher and Miroslava Tegeltija, PhD Student. Technical University of Denmark (Danmarks Tekniske Universitet). Quantitative and semi-quantitative approaches to uncertainty representation.

Matthias C. M. Troffaes. Expert on decision making under uncertainty with imprecise probability; applications in engineering and environmental sciences; avid gamer. Associate Professor (Reader), Durham University, UK.

External reviewer:

EFSA, Assessment and Methodological Support Unit, Olaf Mosbach-Schulz. Implementing uncertainty analysis in food and feed risk assessment. Training programs on probabilistic judgements for external experts.

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The 3rd Nordic Chapter Risk conference in Finland – Call for abstracts

The Nordic Chapter of the Society for Risk Analysis (SRA) invites abstracts to the 3rd conference which will be held at Aalto University, Espoo, Finland, November 2-3, 2017. (20 minutes’ bus trip from Helsinki).

The theme of the conference “Risk and Security” highlights the role risk plays in relation to security.

See more on the conference web

Submit your abstract before 15th of September 2017 !

 

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The Bayes@ concept

Bayes@: Approachable mini conferences on applied Bayesian statistics

A Bayes@ mini-conference is a (more or less) local one day event bring together people working with or interested in Bayesian methods. Bayes@ events aims at being accessible to people with little experience of Bayesian methods while still being relevant to experienced practitioners. The focus is on how Bayesian methods are used in research and industry, what the advantages and challenges are with using Bayesian methods, and how Bayesian methods can be used and taught in a better way.

The first Bayes@ event was organized in Lund, Sweden by Rasmus Bååth and Ullrika Sahlin in 2014 when they noticed that, while many people use Bayesian methods at the university, they were spread all over campus. Rasmus and Ullrika thought: Why not meet up and share experiences and tips in a one day mini-conference? And so they did.

Read more on the github page bayesat.github.io/

Contact Ullrika or Rasmus if you are interested in arranging a Bayes@ conference. Have fun : )

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Extreme events in the real world and in the mind

My sister caught me in the act explaining different types of uncertainty

Last Friday April 28th I gave my docenture lecture for Environmental Science at Lund University. I talked about the importance of considering extreme and rare events in environmental management. It was fun. Thanks all for coming. My presentation is here

Abstract: Have you ever missed a train or got stunned by a surprise you did not expect to happen? In retrospect, could you have done something to prevent bad things from happening and still get the good bits? These questions are valid, not only for our daily decisions, but also for decisions on how to manage the natural environment for conservation, safety or sustainability. To help out, there are theories for considering the extreme, rare or unlikely in decision making, with applications to the management of environmental systems.

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Lundaloppet predictive challenge 2017

Forecasting with uncertainty

Guess your time in Lundaloppet 2017 and express your uncertainty in your running time in which way you want!

After the race on May 13th in Lund and the actual running times are known, we will evaluate the performances of all predictions. The results will be presented in June, where we will hand out several awards.

Make your forecast here 25 people have guessed their running time. Thank you all for participating. 

The challenge is open for anyone

The purposes with the challenge are to highlight what it means to forecast with uncertainty and collect examples of how people prefer to express uncertainty. Any way to express uncertainty is allowed, ranging from qualitative to quantitative, using probabilistic and non-probabilistic descriptions.

The participants are asked to guess their own running time and are therefore basing the forecast on best available (expert’s) knowledge. The nature of the question force them to think about uncertainty as their own, which demonstrate the subjective nature of uncertainty. Finally, participants are asked to rate their confidence in their forecasts, which is a more frequently occurring way to judge strength in predictive knowledge.  

The Lundaloppet predictive challenge was previously been given 2014 with great success.

This challenge is organized by the research group UnEviL (Uncertainty and Evidence Lab) at the Centre of Environmental and Climate research and is an activity of the BECC action group: Evidence relying on simulation models and expert judgment.

For more info contact ullrika.sahlin@cec.lu.se

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The dawn of the new research group “UnEviL”

Uncertainty and Evidence Lab is the name of a new research group at Lund University. The group is led by Ullrika Sahlin at the Centre of Environmental and Climate Research.

Can we trust what we see or believe?

This research group focus on the management of uncertainty and evidence in risk analysis, evidence synthesis, decision analysis and, never the least, predictive sciences. This include discussing and applying principles and methods for learning, forecasting and decision analysis under different strengths in knowledge. We seek to apply the principles and methods on conceptual and actual problems and we are not limited to a specific application.

The research group, ongoing activities and output are presented at Lund University research portal

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Bayes@Lund2017 20th April

The program for Bayes@Lund2017 is now ready

Follow us at #bayeslund17 on twitter

We have uploaded videos of some of the talks on YouTube. Unfortunately not all talks have been recorded because of technical issues.

Richard McElreath — Bayesian Statistics without Frequentist Language: https://youtu.be/yakg94HyWdE
Martin Stjernman — Joint species modelling. Beautiful in theory, tricky in practice: https://youtu.be/KL9iKx0UKco
Shravan Vasishth — Finite mixture modeling: a case study involving retrieval processes in sentence comprehension: https://youtu.be/02YFuxJmyEI
Mark Andrews –Teaching Bayesian methods to social scientists: https://youtu.be/_gAg9UG9RCA
Stefan Wiens — Making the most of your ANOVAs: From NHST to Bayesian analyses: https://youtu.be/umjX4rnxAm0
Judith Bütepage — Learning to make decisions under uncertainty: https://youtu.be/1nz_96gxFvw

Ullrika Sahlin — Using expert’s knowledge in Bayesian analysis: link to presentation no video unfortunately Sahlin_BayesatLund2017

We start in room MA4, Maths building Annex, Sölvegatan 20. We also have a booklet of the abstracts programBayes@Lund2017 maps and tips.

[for the tutorial on 19 April go here]

08.30-9.00 Registration

09.00-9.10 Welcome and an overview of Bayesian activities in Lund: Umberto Picchini and Ullrika Sahlin

9.10-10.05 Keynote talk Darren Wilkinson: Hierarchical modelling of genetic interaction in budding yeast

10.05-10.30 coffee break

Bayesian Analysis I

10.30-10.55 Stefan Wiens, Making the most of your ANOVAs: From NHST to Bayesian analyses

10.55-11.20 Martin Stjernman, Joint species modelling — beautiful in theory, tricky in practice

11.20-11.45 Shravan Vasishth, Finite mixture modeling: a case study involving retrieval processes in sentence comprehension

11.45-13.05 Lunch break  

13.05-14.00 Keynote talk Richard McElreath: Understanding Bayesian statistics without frequentist language

Decisions and Teaching

14.00-14.25 Judith Butepage: Learning to make decisions under uncertainty

14.25-14.50 Mark Andrews: Teaching Bayesian Data Analysis to Social Scientists

14.50-15.10 coffee break

Parallel Sessions

Bayesian Analysis II (room MA7)

15.10-15.35 Thomas Hamelrick: Potentials of mean force for protein structure prediction: from hack to math

15.35-16.00 Junpeng Lao: Statistical Inferences of Eye movement data using Bayesian smoothing

Teaching Bayes (room MA6)

15.10-15.35 Richard Torkar: Convincing researchers to transition to Bayesian statistics – the case of software engineering

15.35-16.00 Bertil Wegmann: Experiences from teaching Bayesian inference to students familiar with frequentist statistics

all back in room MA7 for the final session

Bayesian Analysis III (room MA7)

16.05-16.30 Erik Lindström: Multilevel Monte Carlo methods for inference in multivariate diffusions

16.30-16.55 Ullrika Sahlin: Using expert’s knowledge in Bayesian analysis

Funding from the research schools BECC and COMPUTE is greatly appreciated

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Workshop on Bayesian Networks for risk assessment and decision making

We had a successful workshop on Bayesian Networks in risk assessment and decision making in Lund March 28 and 29th, 2017.

This workshop was financed by the research school ClimBEco.

Local organisers were Indrani Hazel Govender and Ullrika Sahlin.

Tuesday March 28th

Introduction to Bayesian Networks for risk and impact assessment to support decision making.

This introduction was attended by 30 people. It was an interactive and intensive introduction of BNs led by Wayne Landis, Western Washington University, United States, and Ullrika Sahlin, Lund University Centre of Environmental and Climate Research, Lund, Sweden. The participants were provided a theoretical background together with hands on exercises from risk and impact assessments and decision making.

Wayne telling his story about why Bayesian Networks is useful

Ullrika dicussing the step to inform the parameters of your network

 

 

 

 

 

 

 

 

 

More information with links to the presentations is available here:

http://evidence.blogg.lu.se/workshop-introduction-to-bayesian-networks-for-risk-and-impact-assessment-to-support-decision-making/

Final session at the pub

Wednesday March 29th

Open seminar with following workshop on Applications and future developments of Bayesian Networks in risk and impact assessments and environmental decision making.

Talks and abstracts are available at the website:

http://evidence.blogg.lu.se/applications-and-future-developments-of-bayesian-networks-in-risk-and-impact-assessments-and-environmental-decision-making/

Wayne is showing one of his conceptual models for assessing risk.

Anna presented her PhD project using Bayesian Networks

A final line up of those left to the bitter but happy end.

more info – contact Ullrika Sahlin at cec.lu.se

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Studentprojekt: Tuggummin som en indikator för nedskräpning i urbana miljöer

Studenter sökes för ett projekt under våren 2017

Detta projekt går ut på att ta reda på hur förekomst av tuggummin förhåller sig till förekomst av skräp i urbana miljöer samt att undersöka i vilken takt nedskräpning sker på olika typer av platser i städer.

Länkar till hur tuggumin uppmärksammas
http://www.dailymail.co.uk/news/article-2844929/Chewing-gum-companies-help-pay-60million-bill-cleaning-mess-streets-say-councils.html

Projektet innehåller en fältstudie där mängd och ålder på tuggumin undersöks för att skatta var och hur snabbt tuggumin hamnar på gatan och om depositionshastigheten av tuggumin beror på hur många tuggumin som redan finns.  I fältstudien undersöks också förekomst av annat skräp, med syftet att ta reda på om tuggummin är en bra indikator för nedskräpning. Skräp som papper, plast och till en viss del fimpar, har en tendens att flytta på sig, medan tuggummin ligger kvar. Om det finns ett starkt samband kan det ge grund till att föreslå att tuggummin är en bra indikator för nedskräpning.

Studien är en upprepning av en liknande studie gjord i stora städer i England, vilket möjliggör en jämförelse.

Projektet kommer att utöver fältarbete, innehålla en del arbete med GIS.

Det är möjligt att dela upp projektet mellan flera studenter.

Handledare Peter Olsson och Ullrika Sahlin

skicka mail direkt till ullrika.sahlin@cec.lu.se och Peter.olsson@cec.lu.se om du är intresserad

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Applications and future developments of Bayesian Networks in risk and impact assessments and environmental decision making

Open seminar and workshop

When: Wednesday March 29th 2017. From 10 to 14, followed by discussions

Where: The Blue Hall and the Red Room in the Ecology Building, Lund University, Sölvegatan 37, Lund, Sweden

Scroll down to view the list of talks and links to presentations

Background

Bayesian Networks is a type of probabilistic modelling with wide applications in science and decision making. BNs is a modelling framework that enable us to integrate evidence to inform decisions, based on causal relations between decision, states and impact variables. BNs allows integration of data and expert knowledge.

This workshop will demonstrate the application of BNs in risk and impact assessment and environmental management, and, discuss and critically reflect on the developments and applications of BNs in research and decision making, through the use of case studies.

Invited speakers

Wayne Landis, Institute of Environmental Toxicology, Huxley College of the Environment, Western Washington University, United States. Wayne is a Professor in Environmental Science and a Director of the Institute of Environmental Toxicology at Western Washington University. He has over 20 years of experience in ecological risk assessment research, using Bayesian networks to guide decision making. https://huxley.wwu.edu/people/landis

Sakari Kuikka, University of Helsinki, Finland. Sakari is a Professor in fisheries science and a head of the Fisheries and Environmental Management Group research group. http://www.helsinki.fi/science/fem/kuikka.html

Talks

Adaptive management using Bayesian tools to meet the challenges of uncertainty, climate change and the challenge of making decisions – Wayne Landis

Talk: BN Workshop Wayne Landis

Abstract: Environmental contamination, invasive species, changes in technology, and climate change are individually management challenges, but in reality, each intersects with the other.  This interaction is coupled with dramatic social changes in Europe, North America and across the world.  The question becomes how to effectively manage this mélange of factors within the cacophony of social norms.  Terms like “risk” and “uncertainty” also have multiple meanings, often with emotional undertones.  This talk will introduce the approach of using a Bayesian net based risk assessment framework coupled with an adaptive management framework.  Bayesian networks can be built to include multiple stressors affecting endpoints that represent ecological status and human well-being.  The process of adaptive management includes an initial risk assessment coupled to specific predictions regarding management options (hypotheses), followed by observation of the environment, and reconstruction of the Bayesian network to update the model.  The approach is closely coupled to decision making and is intended to be adaptable.  One of the goals in this approach is to accept uncertainty as a normal situation both in the understanding of the managed system, the efficacy of the management solutions, and in the societal norms.

Learning chains in oil spill risk analysis – Sakari Kuikka

Talk: BN Workshop Sakari Kuikka

Abstract: We review the experience obtained in developing integrative Bayesian models in interdisciplinary risk analysis focusing on oil spill in the Gulf of Finland. Moreover, we also discuss the future challenges in this demanding modeling task. We have applied Bayesian models to the oil spill risk analysis in interdisciplinary questions. Bayesian belief networks are flexible tools that can take into account the different research traditions and the various types of information sources. One of the advantages of using Bayesian decision analysis for management is that the uncertainty estimates are scientifically justified. Moreover, the Bayesian inference offers and important possibility to learn effectively from many sources of information, and the results of one integrative model can, and we argue that they should, be used as priors for next accidents so that the learning component from previous spills is as high as possible. Especially in cases where society is assumed to be risk averse, the uncertainty estimates have a crucial role.

Assessing multiple climate change impacts on water quality: a Bayesian Networks approach – Anna Sperotto

Talk: BN Workshop Anna Sperotto

Abstract: Bayesian Networks are employed for the implementation of a multi-risk model to assess cascading impacts induced by multiple stressors on water quality taking into account multiple climate and land use scenarios. Specifically, Bayesian Networks are applied as a meta-modelling tool for structuring and combining the information coming from existing hydrological models simulations, climate change and land use scenarios and to prioritize the contribute of different stressors on water quality status.

A Bayesian approach for safety barrier portfolio optimization – Alessandro Mancuso

Talk: BN Workshop Alessandro Mancuso

Abstract: In the framework of Probabilistic Risk Assessment (PRA), we develop a method to support the selection of cost-effective portfolios of safety measures. This method provides a systemic approach to determining the optimal portfolio of safety measures that minimizes the risk of the system and thus provides an alternative to using risk importance measures for guiding the selection of safety measures. We represent combinations of events leading to system failure with Bayesian Belief Networks (BBNs) which can be derived from traditional Fault Trees (FTs) and are capable of encoding event dependencies and multi-state failure behaviors. We also develop a computationally efficient enumeration algorithm to identify which combinations (portfolios) of safety measures minimize the risk of failure at different costs of implementing the safety measures. The method is illustrated by revisiting an earlier case study concerning the airlock system of a CANDU Nuclear Power Plant (NPP). The comparison of results with those of choosing safety measures based on risk importance measures shows that our approach can lead to considerably lower residuals risks at different cost levels.

Application of Bayesian Networks in integrated water resource management – Hazel Indrani Govender

Talk: BN Workshop Hazel Govender

Abstract: Water catchments are complex, with water resources receiving impacts from a vast range of land-use activities. Integrated Water Resource Management (IWRM) is a holistic approach that attempts to integrate the sustainable management of the water, land and related resources within the broader socio-economic and political context.  Risk assessment at a catchment scale, requires the consideration of multiple stressors and many causal relationships that result from the interactions between the components of ecosystems.  The Relative Risk Model, using Bayesian Networks (BNs), is used to assess the risks in a water stressed, economically critical water catchment in South Africa.  The focus on the study area by the authorities and government, has facilitated a number of research efforts and collaborations.  This is bringing together experts and a range of stakeholders in working towards protection of the water resources in the catchment.  This is beneficial to the application of Bayesian Networks as the information and data resulting from these research efforts can contribute to knowledge gaps and missing data.  This can facilitate updating of the BNs and contribute to an adaptive management approach to protecting water resources in the catchment.

What is needed to get Bayesian Networks robust to weaknesses in knowledge? – Ullrika Sahlin

Talk: BN Workshop Ullrika Sahlin

Abstract: Also the sun has its spots. Bayesian Networks are useful, but has its limitations. I will mention some problems with BNs coming from weaknesses in knowledge. Instead of leaving you in total misery, I will end with some suggestions on how to deal with these issues without totally abandoning Bayesian Networks.

This workshop is funded by the research school ClimBEco 

Local organisers were Indrani Hazel Govender and Ullrika Sahlin

 

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