Using Bayesian belief networks in adaptive management

2006 ◽  
Vol 36 (12) ◽  
pp. 3104-3116 ◽  
Author(s):  
J Brian Nyberg ◽  
Bruce G Marcot ◽  
Randy Sulyma
Keyword(s):  
Adaptive Management ◽  
Bayesian Belief Networks ◽  
Influence Diagrams ◽  
Belief Networks ◽  
Alternative Management ◽  
Time Dynamic ◽  
Decision Networks ◽  
Management Actions ◽  
Modelling Techniques ◽  
Management Policies

Bayesian belief and decision networks are modelling techniques that are well suited to adaptive-management applications, but they appear not to have been widely used in adaptive management to date. Bayesian belief networks (BBNs) can serve many purposes, from illustrating a conceptual understanding of system relations to calculating joint probabilities for decision options and predicting outcomes of management policies. We describe the nature and capabilities of BBNs, discuss their applications to the adaptive-management process, and present a case example of adaptive management of forests and terrestrial lichens in north-central British Columbia. We recommend that those unfamiliar with BBNs should begin by first developing influence diagrams with relatively simple structures that represent the system under management. Such basic models can then be elaborated to include more variables, the mathematical relations among them, and features that allow assessment of the utility of alternative management actions or strategies. Users of BBNs should be aware of several important limitations, including problems in representing feedback and time–dynamic functions. Nevertheless, when properly used, Bayesian networks can benefit most adaptive-management teams by promoting a shared understanding of the system being managed and encouraging the rigorous examination of alternative management policies.

Bayesian Belief Networks in IT Investment Decision Making

2017 ◽  
pp. 75-107 ◽  
Keyword(s):  
Decision Making ◽  
Investment Decision ◽  
Bayesian Belief Networks ◽  
Influence Diagrams ◽  
Belief Networks ◽  
It Investment ◽  
Investment Decision Making ◽  
Availability Modeling

This section aims at describing the concept of Bayesian Belief Networks (BBN), building principles and application of BBN and influence diagrams, as well as the reasons why BBN are considered an adequate tool for IS availability modeling.

scholarly journals CASH FLOW FORECASTING WITH RISK CONSIDERATION USING BAYESIAN BELIEF NETWORKS (BBNS)

2017 ◽  
Vol 23 (8) ◽  
pp. 1045-1059 ◽  
Author(s):  
Mostafa KHANZADI ◽  
Ehsan ESHTEHARDIAN ◽  
Mahdiyar MOKHLESPOUR ESFAHANI
Keyword(s):  
Risk Factors ◽  
Cash Flow ◽  
Construction Projects ◽  
Bayesian Belief Networks ◽  
Influence Diagrams ◽  
Belief Networks ◽  
Cash Flow Management ◽  
Construction Companies ◽  
Cash Flow Forecasting

Cash-flow management is very important for contractors given that inadequate cash resources typically are the main causes for bankruptcy of construction companies. In comparison to most other industries, the construction industry is severely plagued by risk, and the success of construction projects usually depends on valuating all risks. However, conventional methods suggested by extant research on cash flow forecasting do not consider comprehensive identifica­tion of risk factors, interactions between the factors, and simultaneous occurrences of the factors. This study introduced a simple and appropriate probabilistic cash flow forecasting model using Bayesian Belief Networks (BBNs) to avoid bankruptcy of contractors by considering influence diagrams and risk factors that affect a project. Workability and reli­ability of the proposed approach was tested on an important building construction project in Iran as a real case study, and the results indicated that the model performed well.

Guidelines for developing and updating Bayesian belief networks applied to ecological modeling and conservation

2006 ◽  
Vol 36 (12) ◽  
pp. 3063-3074 ◽  
Author(s):  
Bruce G Marcot ◽  
J Douglas Steventon ◽  
Glenn D Sutherland ◽  
Robert K McCann
Keyword(s):  
Bayesian Belief Networks ◽  
Management Decision ◽  
Influence Diagrams ◽  
Belief Networks ◽  
Validation Data ◽  
Factors Affecting ◽  
Expert Review ◽  
Management Decision Making ◽  
Practical Guidelines ◽  
Level Model

Bayesian belief networks (BBNs) are useful tools for modeling ecological predictions and aiding resource-management decision-making. We provide practical guidelines for developing, testing, and revising BBNs. Primary steps in this process include creating influence diagrams of the hypothesized "causal web" of key factors affecting a species or ecological outcome of interest; developing a first, alpha-level BBN model from the influence diagram; revising the model after expert review; testing and calibrating the model with case files to create a beta-level model; and updating the model structure and conditional probabilities with new validation data, creating the final-application gamma-level model. We illustrate and discuss these steps with an empirically based BBN model of factors influencing probability of capture of northern flying squirrels (Glaucomys sabrinus (Shaw)). Testing and updating BBNs, especially with peer review and calibration, are essential to ensure their credibility and reduce bias. Our guidelines provide modelers with insights that allow them to avoid potentially spurious or unreliable models.

Reflections on the use of Bayesian belief networks for adaptive management

2008 ◽  
Vol 88 (4) ◽  
pp. 1025-1036 ◽  
Author(s):  
Hans Jørgen Henriksen ◽  
Heidi Christiansen Barlebo
Keyword(s):  
Adaptive Management ◽  
Bayesian Belief Networks ◽  
Belief Networks

Strategies to improve implementation of adaptive management practices for restoration in coastal Louisiana

Shore & Beach ◽  
2020 ◽  
pp. 83-91
Author(s):  
Tim Carruthers ◽  
Richard Raynie ◽  
Alyssa Dausman ◽  
Syed Khalil
Keyword(s):  
Adaptive Management ◽  
Management Practices ◽  
Numerical Models ◽  
The United States ◽  
State Agency ◽  
Coastal Protection ◽  
Coastal Restoration ◽  
Management Actions ◽  
Coastal Louisiana ◽  
Restoration Effort

Natural resources of coastal Louisiana support the economies of Louisiana and the whole of the United States. However, future conditions of coastal Louisiana are highly uncertain due to the dynamic processes of the Mississippi River delta, unpredictable storm events, subsidence, sea level rise, increasing temperatures, and extensive historic management actions that have altered natural coastal processes. To address these concerns, a centralized state agency was formed to coordinate coastal protection and restoration effort, the Coastal Protection and Restoration Authority (CPRA). This promoted knowledge centralization and supported informal adaptive management for restoration efforts, at that time mostly funded through the Coastal Wetlands Planning, Protection and Restoration Act (CWPPRA). Since the Deepwater Horizon (DWH) oil spill in 2010 and the subsequent settlement, the majority of restoration funding for the next 15 years will come through one of the DWH mechanisms; Natural Resource and Damage Assessment (NRDA), the RESTORE Council, or National Fish and Wildlife Foundation –Gulf Environmental Benefit Fund (NFWF-GEBF). This has greatly increased restoration effort and increased governance complexity associated with project funding, implementation, and reporting. As a result, there is enhanced impetus to formalize and unify adaptive management processes for coastal restoration in Louisiana. Through synthesis of input from local coastal managers, historical and current processes for project and programmatic implementation and adaptive management were summarized. Key gaps and needs to specifically increase implementation of adaptive management within the Louisiana coastal restoration community were identified and developed into eight tangible and specific recommendations. These were to streamline governance through increased coordination amongst implementing entities, develop a discoverable and practical lessons learned and decision database, coordinate ecosystem reporting, identify commonality of restoration goals, develop a common cross-agency adaptive management handbook for all personnel, improve communication (both in-reach and outreach), have a common repository and clearing house for numerical models used for restoration planning and assessment, and expand approaches for two-way stakeholder engagement throughout the restoration process. A common vision and maximizing synergies between entities can improve adaptive management implementation to maximize ecosystem and community benefits of restoration effort in coastal Louisiana. This work adds to current knowledge by providing specific strategies and recommendations, based upon extensive engagement with restoration practitioners from multiple state and federal agencies. Addressing these practitioner-identified gaps and needs will improve engagement in adaptive management in coastal Louisiana, a large geographic area with high restoration implementation within a complex governance framework.

Using Bayesian network models to incorporate uncertainty in the economic analysis of pollution abatement measures under the water framework directive

2005 ◽  
Vol 5 (6) ◽  
pp. 95-104 ◽  
Author(s):  
D.N. Barton ◽  
T. Saloranta ◽  
T.H. Bakken ◽  
A. Lyche Solheim ◽  
J. Moe ◽  
...  
Keyword(s):  
Water Quality ◽  
At Risk ◽  
Economic Valuation ◽  
Pollution Abatement ◽  
Network Models ◽  
Cost Effective ◽  
Bayesian Belief Networks ◽  
Probabilistic Interpretation ◽  
Belief Networks ◽  
Benefit Cost Analysis

The evaluation of water bodies “at risk” of not achieving the Water Framework Directive's (WFD) goal of “good status” begs the question of how big a risk is acceptable before a programme of measures should be implemented. Documentation of expert judgement and statistical uncertainty in pollution budgets and water quality modelling, combined with Monte Carlo simulation and Bayesian belief networks, make it possible to give a probabilistic interpretation of “at risk”. Combined with information on abatement costs, a cost-effective ranking of measures based on expected costs and effect can be undertaken. Combined with economic valuation of water quality, the definition of “disproportionate cost” of abatement measures compared to benefits of achieving “good status” can also be given a probabilistic interpretation. Explicit modelling of uncertainty helps visualize where research and consulting efforts are most critical for reducing uncertainty. Based on data from the Morsa catchment in South-Eastern Norway, this paper discusses the relative merits of using Bayesian belief networks when integrating biophysical modelling results in the benefit-cost analysis of derogations and cost-effectiveness ranking of abatement measures under the WFD.

scholarly journals Using Bayesian Belief Networks to assess the influence of landscape connectivity on ecosystem service trade-offs and synergies in urban landscapes in the UK

2021 ◽  
Author(s):  
James D. Karimi ◽  
Jim A. Harris ◽  
Ron Corstanje
Keyword(s):  
Functional Connectivity ◽  
Ecosystem Service ◽  
Urban Areas ◽  
Landscape Connectivity ◽  
Structural Characteristics ◽  
Bird Species ◽  
Bayesian Belief Networks ◽  
Belief Networks ◽  
Patch Area ◽  
Trade Offs

Abstract Context Landscape connectivity is assumed to influence ecosystem service (ES) trade-offs and synergies. However, empirical studies of the effect of landscape connectivity on ES trade-offs and synergies are limited, especially in urban areas where the interactions between patterns and processes are complex. Objectives The objectives of this study were to use a Bayesian Belief Network approach to (1) assess whether functional connectivity drives ES trade-offs and synergies in urban areas and (2) assess the influence of connectivity on the supply of ESs. Methods We used circuit theory to model urban bird flow of P. major and C. caeruleus at a 2 m spatial resolution in Bedford, Luton and Milton Keynes, UK, and Bayesian Belief Networks (BBNs) to assess the sensitivity of ES trade-offs and synergies model outputs to landscape and patch structural characteristics (patch area, connectivity and bird species abundance). Results We found that functional connectivity was the most influential variable in determining two of three ES trade-offs and synergies. Patch area and connectivity exerted a strong influence on ES trade-offs and synergies. Low patch area and low to moderately low connectivity were associated with high levels of ES trade-offs and synergies. Conclusions This study demonstrates that landscape connectivity is an influential determinant of ES trade-offs and synergies and supports the conviction that larger and better-connected habitat patches increase ES provision. A BBN approach is proposed as a feasible method of ES trade-off and synergy prediction in complex landscapes. Our findings can prove to be informative for urban ES management.

Problems Confronting Fishing Industries Relative to Management Policies Adopted by Governments

1973 ◽  
Vol 30 (12) ◽  
pp. 2486-2489
Author(s):  
G. H. Elliot
Keyword(s):  
Fisheries Management ◽  
International Level ◽  
Full Understanding ◽  
Alternative Management ◽  
Fishery Resources ◽  
International Action ◽  
The Future ◽  
Future Viability ◽  
Management Policies ◽  
Fishing Fleets

With increase in numbers, size, and effectiveness, fishing fleets have depleted important stocks of fish, and strong international action by governments is imperative for the future viability of fishery resources. The author favors a system of an overall quota of fish, with individual quotas for boats and plants, and predicts that this will become "the accepted method of running fisheries" in 20 years. He discusses how best to organize such a system, with full consultation between governments and their national fishing industries as well as at the international level. For efficient handling of the complex issues involved and a full understanding of them, he suggests that governments should appoint to their fisheries ministries officers who have specialized in fisheries management and are able to analyze the situation in depth and advise the administrators on the implications of alternative management policies. The controls over fishing that he advocates are essential because "free fish means eventually no fish."

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