How do you choose between decision trees and influence diagrams for complex problems?

Decision visual tools used to map out decisions and their potential consequences. They consist of branches that represent choices, chance events, and outcomes, providing a clear structure for analyzing complex problems. Each path in a decision tree helps identify possible scenarios, weighing risks and rewards to aid in making informed decisions. They're particularly useful for breaking down intricate issues into manageable parts, allowing for systematic exploration and evaluation of each option's potential impact.

Ao enfrentar problemas complexos, a escolha entre árvores de decis?o e diagramas de influência depende do que estou tentando alcan?ar. Se busco uma abordagem estruturada para decis?es específicas, opto por árvores de decis?o, pois elas me ajudam a visualizar as possíveis consequências e a tomar decis?es informadas. Por outro lado, se quero entender melhor as intera??es entre diferentes variáveis e como elas influenciam o resultado, os diagramas de influência se tornam mais úteis. Em ambos os casos, considero o contexto da situa??o e a equipe envolvida, sempre buscando a melhor ferramenta para facilitar a compreens?o e a colabora??o.

Uma árvore de decis?o é uma representa??o gráfica que ilustra um processo de decis?o sequencial. Nela, cada nó representa uma escolha, uma chance ou um resultado, e as ramifica??es conectam os nós, mostrando as probabilidades ou valores associados a cada op??o. O processo come?a com um nó raiz, que indica o ponto de decis?o inicial, e termina em nós de folha, que representam os resultados finais ou recompensas. Essa ferramenta é útil para identificar e comparar os valores esperados de diferentes alternativas, permitindo encontrar a estratégia ideal que maximiza ou minimiza o objetivo desejado.

Safety models…it depends on the context… There are about 30 to 40 (risk-related) models that have been used to: describe so-called accident theories, to reconstruct accidents, to enable hazard analysis and risk assessment, to conduct safety-related communications, to illustrate complexities of systems. A good model can enable a systemized approach towards safety efforts; a model can describe the project, product, process. A model can be used to illustrate complex interrelationships and interfaces. Some models have been used as check lists. In using models an analyst can look at the problem from a different point of view. Models can be good or bad. They are abstractions at different levels of induction or deduction.

An influence diagram is a graphical representation of a decision problem that simplifies the structure of complex decision-making processes. It consists of three main components: -Decision nodes. -Chance nodes: decision. -Value nodes. Example Imagine a company deciding whether to launch a new product. The decision to launch (Decision Node) depends on the market demand, which is uncertain (Chance Node). The company’s objective is to maximize profit (Value Node), which is influenced by both the decision and the market’s response. Arrows would connect the decision node to the chance node (since the decision impacts market demand) and from both the decision and chance nodes to the value node (since profit depends on both).

Um diagrama de influência é uma vers?o simplificada de uma árvore de decis?o, que agrupa os nós em três tipos: nós de decis?o, nós de acaso e nós de valor. Os nós de decis?o representam as escolhas a serem feitas, enquanto os nós de acaso refletem eventos incertos que influenciam o problema. Os nós de valor indicam os objetivos ou critérios que se deseja otimizar. Os arcos conectam os nós, mostrando as rela??es causais ou lógicas entre eles. Essa ferramenta ajuda a capturar a estrutura essencial e as dependências do problema, reduzindo a complexidade e a redundancia presentes em uma árvore de decis?o.

Visual modeling tools like influence diagrams are vital for simplifying complex decisions. In platforms like Analytica, you can literally see the model, with the diagram updating automatically when mathematical relationships change. This means it’s not just a picture—it contains the model, making it a live representation of decisions and their outcomes. With built-in Monte Carlo simulation, influence diagrams go beyond decision trees, offering dynamic, probabilistic insights. This approach has transformed my work, enabling more informed, strategic decision-making by visualizing critical interdependencies.

Decision trees can handle far fewer details and scenarios that influence diagrams. An influence diagram can handle dozens of variables comfortably, and then you can evaluate it in a nice Monte Carlo engine like Analytica. A decision tree becomes impossible to interpret when you get seven layers of branches, and computationally tractable at about a dozen. Do not use decision trees to formulate your problem. If you have analyzed your problem and developed insight, and want to convey the interaction of a few key variables for senior decision makers, you can retroactively develop a tree to do so. But this only works because you have already figured out which are the important variables that need to be shown in the tree.

In my experience, using decision trees and influence diagrams effectively often involves two powerful tools: tornado analysis and the Expected Value of Perfect Information (EVPI). Tornado analysis helps identify the variables that truly matter by showing which uncertainties have the greatest impact on the outcome. Once key drivers are identified, EVPI becomes invaluable for allocating resources, as it quantifies the value of eliminating uncertainty. This helps organizations focus their analysis and research budgets where it will provide the greatest return, ensuring efficient and informed decision-making.