BEGIN:VCALENDAR VERSION:2.0 PRODID:-//talks.cam.ac.uk//v3//EN BEGIN:VTIMEZONE TZID:Europe/London BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:19700329T010000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:19701025T020000 RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT CATEGORIES:Isaac Newton Institute Seminar Series SUMMARY:Decision-making under uncertainty: Using subjectiv e probabilistic judgements for decision support in pollinator abundance and food security - Martine Barons (University of Warwick) DTSTART;TZID=Europe/London:20190704T121000 DTEND;TZID=Europe/London:20190704T123000 UID:TALK126808AThttp://talks.cam.ac.uk URL:http://talks.cam.ac.uk/talk/index/126808 DESCRIPTION:Hunger and food poverty is on the increase even in developed nations like the UK\, USA\, Canada & \; Australia. With a growing population and a fin ite planet\, there is urgent need for action\, but in such a large\, complex system identifying the most effective action requires decision support.
Food security exists when all people\, at all times\, have physical and economic access to suff icient\, safe and nutritious food to meet their di etary needs and food preferences for an active and healthy life.
In order to provide decision support\, it is necessary to elicit probability di stributions to evaluate subjective expected utilit y scores associated with ameliorating policies tha t might be enacted. When the underlying process mo del is extremely large and complex\, this brings i ts own peculiar challenges. It is first necessary to elicit the overall\, agreed structure describin g in broad terms the underlying nature of the syst em from representatives of all domain experts acro ss the system as a whole. We have now shown that t his can be done formally and consistently with pro bability models if the elicitations concern the el icitation of dependences &ndash\; formally termed irrelevances (Smith\, Barons and Leonelli (2016)). Within a probability model\, these irrelevance st atements then transform into assertions about vari ous conditional independence statements. These\, i n turn\, can be used to determine how the system c an be divided up into (conditionally) independent segments. The quantitative expert judgements asso ciated with each segment of the process can then b e delegated to a relevant panel of experts. The im plicit (albeit virtual) owner of beliefs expressed in the system will be referred to as the supraBay esian \, meaning that the decision-making group a cts as a single person would and it is her coheren ce that we are concerned with. Under suitable cond itions it can then be shown that the elicited over arching structure can compose these judgments toge ther to form a coherent probabilistic model to sco re different options available to the user\, terme d an integrating decisions support system (IDSS).

One element of the overarching food pove rty models is food supply\, and key to parts of th is is an abundant and healthy population of pollin ating insects to pollination services for food. In 2014 the UK government undertook a consultation a nd produced their pollinator strategy for the next 10 years &ldquo\;to see pollinators thrive\, prov iding essential pollination services and benefits for food production\, the wider environment and ev eryone.&rdquo\; However\, the evidence base on th e complex system driving pollinator vigour and num bers is patchy and held in disparate domains of ex pertise\, making the evaluation of policy options problematic. In this talk I will describe how we are in the process of developing an IDSS based on these theoretical developments\, and how a probabi listic model for pollinator abundance incorporatin g structured expert elicitation will then form a s ub-module of this IDSS for policies relating to ho usehold food insecurity.

J. Q. Smith\, M.J . Barons\, and M. Leonelli. Coherent inference for integrating decision support systems. arXiv\, 201 6. http://arxiv.org/abs/150 7.07394.

Co-authors: Jim Q. Smith\, Man uele Leonelli
LOCATION:Seminar Room 1\, Newton Institute CONTACT:INI IT END:VEVENT END:VCALENDAR