Decision Analysis course – Lecture 7 – Structured decision solutions

This post is for students attending the 2015-2016 edition of my Decision Analysis course at University of Namur.

Agenda

– Solutions;
– Solution structure;
– Solution economics.

Slides

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Decision Analysis course – Lecture 6 – Elicitation

This post is for students attending the 2015-2016 edition of my Decision Analysis course at University of Namur.

Agenda

– Role of elicitation in the analysis of unstructured decision problems;
– Elicitation techniques;
– Clarification, prioritisation, and validation of elicited information.

Slides

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Decision Analysis course – recommended content 3 – On mercenaries, globalisation, delayed bubbles, and nostalgia

This post is for students attending the 2015-2016 edition of my Decision Analysis course at University of Namur.

Below are some recommendations on what to read and listen to this week. These recommendations should be taken as-is: they are optional, sometimes only remotely related to the course, but still interesting as food for thought on decisions, complex problems, and related topics. Being recommendations, you decide if you want to pay attention to them. It will not influence your evaluation.

– LSE Podcast: The Modern Mercenary: private armies and what they mean for world order – On the business of privatising armed forces.

– Wired: Stanford’s Self-Driving DeLorean Drifts, Does Killer Donuts – Autonomous decision-making with style.

– The Atlantic: The Downsizing of the American Dream – Same topic comes back every time US elections approach, and nostalgia kicks in. Remember, memories are selective.

– The Guardian: Is the dotcom bubble about to burst (again)? – Yes, it is, somehow this keeps getting delayed.

– The New York Times: The Hypocrisy of ‘Helping’ the Poor – Some win, looks like a lot lose.

– London Review of Books: Tycooniest – This one is just for fun. Favorite part: “The National Journal has worked out that if Trump had just put his father’s money in a mutual fund that tracked the S&P 500 and spent his career finger-painting, he’d have $8 billion.” Draw your own conclusions.





Decision Analysis course – Lecture 5 – Unstructured decision problems

This post is for students attending the 2015-2016 edition of my Decision Analysis course at University of Namur.

Agenda

– Unstructured decision problems;
– Unstructured decision problems in business;
– Generic problem-solving method;
– Solution structure.

Slides

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Mandatory readings for next lecture

Cooke, Nancy J. “Varieties of knowledge elicitation techniques.” International Journal of Human-Computer Studies 41.6 (1994): 801-849.

If you want to know more

Ericsson, K. Anders, and Andreas C. Lehmann. “Expert and exceptional performance: Evidence of maximal adaptation to task constraints.” Annual review of psychology 47.1 (1996): 273-305.

Coase, Ronald H. “The nature of the firm.” economica 4.16 (1937): 386-405.

Simon, Herbert A. “Organizations and markets.” The Journal of Economic Perspectives (1991): 25-44.

Williamson, Oliver E. “Comparative economic organization: The analysis of discrete structural alternatives.” Administrative science quarterly (1991): 269-296.

Malone, Thomas W. “Modeling coordination in organizations and markets.” Management science 33.10 (1987): 1317-1332.

Malone, Thomas W., and Kevin Crowston. “The interdisciplinary study of coordination.” ACM Computing Surveys (CSUR) 26.1 (1994): 87-119.

Langlois, Richard N. “The vanishing hand: the changing dynamics of industrial capitalism.” Industrial and corporate change 12.2 (2003): 351-385.

Problem to solve for next week

No problem for next week :-)





Decision Analysis course – recommended content 2 – On robotics statistics, city development, overconfidence, NASA, autocracies

This post is for students attending the 2015-2016 edition of my Decision Analysis course at University of Namur.

Below are some recommendations on what to read and listen to this week. These recommendations should be taken as-is: they are optional, sometimes only remotely related to the course, but still interesting as food for thought on decisions, complex problems, and related topics. Being recommendations, you decide if you want to pay attention to them. It will not influence your evaluation.

– LSE Podcast: Why Cities Succeed and Fail Today – Do successful companies go where people do, or is it the other way around?

– International Federation of Robotics: World Robotics 2015 Industrial Robots statistics – On how the reality of robotics is still very far from the Terminator.

– The New York Review of Books: Why Free Markets Make Fools of Us – On phools and phishing, or on decision making when your focus is off.

– Buzzfeed: WeWork Used These Documents To Convince Investors It’s Worth Billions – On odd valuations and overconfidence, more generally on bias in investing.

– Wired: Her Code Got Humans on the Moon—And Invented Software Itself – On Margaret Hamilton, and the early period of software engineering.

– The Verge: Could autonomous ships make the open seas safer? – On how people still die at sea, and how technology could mitigate that.

– New York Times: Why do we tolerate the sins of the Saudis? – On how autocracies are still not a thing of the past and why that is harmful.

– The Verge: Welcome to hell: Apple vs. Google vs. Facebook and the slow death of the web – On the role of ads in incentives to create content online.





Decision Analysis course – Lecture 4 – Argumentation and decisions

This post is for students attending the 2015-2016 edition of my Decision Analysis course at University of Namur.

Agenda

– Argumentation;
– Argument validity;
– Argument soundness;
– Argumentation fallacies;
– Formal (mathematical) models of argument.

Slides

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Mandatory readings for next lecture

McKenna, Christopher D. “The origins of modern management consulting.” Business and Economic History (1995): 51-58.

If you want to know more

Dung, Phan Minh. “On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games.” Artificial intelligence 77.2 (1995): 321-357.

Chesñevar, Carlos Iván, Ana Gabriela Maguitman, and Ronald Prescott Loui. “Logical models of argument.” ACM Computing Surveys (CSUR) 32.4 (2000): 337-383.

Muller, Johannes, and Andrew Hunter. “An argumentation-based approach for decision making.” Tools with Artificial Intelligence (ICTAI), 2012 IEEE 24th International Conference on. Vol. 1. IEEE, 2012.

Problem to solve for next week

No problem for next week :-)





Decision Analysis course – Lecture 3 – Organisational decision-making

This post is for students attending the 2015-2016 edition of my Decision Analysis course at University of Namur.

Agenda

– Organisational decision-making;
– Main models of above;
– Limitations of main models.

Slides

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Mandatory readings for next lecture

Langley, Ann, et al. “Opening up decision making: The view from the black stool.” Organization Science 6.3 (1995): 260-279.

If you want to know more

Simon, Herbert A. “A behavioral model of rational choice.” The quarterly journal of economics (1955): 99-118.

Cohen, Michael D., James G. March, and Johan P. Olsen. “A garbage can model of organizational choice.” Administrative science quarterly (1972): 1-25.

- Mintzberg, Henry, Duru Raisinghani, and Andre Theoret. “The structure of” unstructured” decision processes.” Administrative science quarterly (1976): 246-275.

Problem to solve for next week

View and download





Decision Analysis course – recommended content 1 – On robots, law, murder, design, television, and work culture

This post is for students attending the 2015-2016 edition of my Decision Analysis course at University of Namur.

Below are some recommendations on what to read and listen to this week. These recommendations should be taken as-is: they are optional, sometimes only remotely related to the course, but still interesting as food for thought on decisions, complex problems, and related topics. Being recommendations, you decide if you want to pay attention to them. It will not influence your evaluation.

– LSE Podcast: “Open the Pod Bay Doors, HAL”: Machine Intelligence and the Law – On an interesting notion of machine-assisted decision-making, and its challenges for law, as well as some nice dilemmas on responsibility for damages caused by (future) robots capable of autonomous decision-making.

– LSE Podcast: Black Earth: the Holocaust as history and warning – On a different view of how the maintenance or destruction of states is related to mass murder in World War II.

– The Guardian: Hitler’s world may not be so far away – An extension of the argument from the podcast above, to the role of the state in influencing regulation and incentives for dealing with climate change.

– Wired: The Bizarre, Bony-Looking Future of Algorithmic Design – On using constraint optimisation to automatically produce design alternatives, which designers can then use to inspire their own work.

– London Review of Books: Stop the Robot Apocalypse – On philosophers of benevolent capitalism.

– The Verge: How baseball’s tech team built the future of television – On the relationship between entertainment and technology, and how the latter amplifies the former.

– New York Times: Inside Amazon: Wrestling Big Ideas in a Bruising Workplace – On a questionable work culture at Amazon.





Decision Analysis course – Lecture 2 – Basic method and model

This post is for students attending the 2015-2016 edition of my Decision Analysis course at University of Namur.

Agenda

– Basic Decision Analysis method;
– Basic Decision Analysis model.

Slides

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Mandatory readings for next lecture

Simon, Herbert A. “The structure of ill-structured problems.” Models of discovery. Springer Netherlands, 1977. 304-325.

If you want to know more

Jonassen, David H. “Toward a design theory of problem solving.” Educational technology research and development 48.4 (2000): 63-85.

Jonassen, David, Johannes Strobel, and Chwee Beng Lee. “Everyday problem solving in engineering: Lessons for engineering educators.” Journal of engineering education 95.2 (2006): 139.

Goel, Vinod, and Peter Pirolli. “The structure of design problem spaces.” Cognitive science 16.3 (1992): 395-429.

Problem to solve for next week

View and download





Decision Analysis course – Lecture 1 – Key topics

This post is for students attending the 2015-2016 edition of my Decision Analysis course at University of Namur.

Agenda

– Course basics and evaluation;
– Key questions and topics in decision analysis.

Slides

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Mandatory readings for next lecture

Keeney, Ralph L. “Decision analysis: an overview.” Operations research 30.5 (1982): 803-838.

If you want to know more

Howard, Ronald A. “Decision analysis: practice and promise.” Management science 34.6 (1988): 679-695.

Schoemaker, Paul JH. “The expected utility model: Its variants, purposes, evidence and limitations.” Journal of economic literature (1982): 529-563.

Figueira, José, Salvatore Greco, and Matthias Ehrgott. Multiple criteria decision analysis: state of the art surveys. Vol. 78. Springer Science & Business Media, 2005.

Problem to solve for next week

View and download

HBS Cases

View all cases





Analytic Graphs for Root Cause Analysis: a healthcare case

This text illustrates one way of using Analytic Graphs for Root Cause Analysis, with an example from healthcare.

What is Root Cause Analysis?

Root Cause Analysis is a method for identifying causes of errors, or more generally, of variations in performance.

It was originally developed in psychology and systems engineering.

It is used in such diverse domains as healthcare, manufacturing, and information technology.

There is no conclusive empirical evidence to confirm that doing Root Cause Analysis in fact reduces risk and improves safety. Nevertheless, it remains widely used.[1,2]

Why perform Root Cause Analysis?

The following passage gives an example of when and why Root Cause Analysis is used in in healthcare.

“Preventable mistakes are common in medicine. For example, at 1 hospital, a patient received patient-controlled analgesia (PCA), a combination of local anesthetic and narcotic. The medication was intended to be infused into the epidural space. Instead, a nurse inadvertently connected the tubing to an intravenous catheter, delivering potentially lethal anesthetic into the patient’s bloodstream. What followed were the nurse’s anguish and guilt and, almost as inevitably, the hospital’s root cause analysis (RCA). In the last decade, this process has become the main way medicine investigates mistakes and tries to prevent future mistakes.”[1]

Why use Analytic Graphs for Root Cause Analysis?

In healthcare, experts estimate that one Root Cause Analysis requires between 20 to 90 person-hours to complete.

It requires communication between different people, representation of information about causes and links between causes, explanations for links and causes, and discussion to reach agreement. The following passage illustrates this.

“A root cause analysis should be performed as soon as possible after the error or variance occurs. Otherwise, important details may be missed. All of the personnel involved in the error must be involved in the analysis. Without all parties present, the dis- cussion may lead to fictionalization or speculation that will di- lute the facts.” [2]

Participants in Root Cause Analysis can make Analytic Graphs to record causes and causal links, to use the resulting graphs as documentation, to query these graphs to get answers to questions they have while doing analysis, and so on.

Case

The following passage summarises a safety failure, which led to the application of Root Cause Analysis.

“A laboratory aide was cleaning one of the gross dissection rooms where the residents work. This aide was a relatively new employee who had transferred to the department just a few days prior to the event. When she was cleaning the sink in the dis- section room, she accidentally ran her thumb along the length of a dissecting knife—an injury that required 10 to 15 stitches. Since there had been other less serious accidents in this room and several previous attempts to address the safety issues had not been effective, the department completed a root cause analysis.” [2]

In all graphs below, you can zoom in and out and move the graph around.

First graph: Events and causality

The first graph represents some of the information accumulated in the case.

All links, shown as circles marked with “L”, are labelled “cause” to indicate that each is an instance of the relationship that designates causation. The relationship is binary, irreflexive, antisymmetric, and transitive, that is, the cause relation is a partial order.

Each node is shown as a square, is marked “N” and has two labels:

– One label is “Event” to indicate that the node is an event, that is, an instance of an event class.

– The other label is text describing the event instance.

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

Second graph: Classification of events

The second graph below was made by adding information to the first graph. The additional information is about the source events in the graph, or the events which are the root causes. Each root cause event now has an additional label, used to indicate the category of that event, following the Eindhoven Classification model of system failure [3]. Each label is an abbreviation, as follows:

– OP: Events due to quality and availability of processes, protocols;

– OC: Events due to organizational culture, that is, collective promotion or suppression of specific behaviors;

– HSS: Failures in applying or performing fine motor skills;

– TEX: Technical failures which are beyond control and responsibility of the relevant organization;

– OEX: Organizational failures which are beyond control and responsibility of the relevant organization.

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References

[1] Wu, Albert W., Angela KM Lipshutz, and Peter J. Pronovost. “Effectiveness and efficiency of root cause analysis in medicine.” Jama 299.6 (2008) 685-687. Link to the paper.

[2] Williams, Patricia M. “Techniques for root cause analysis.” Proceedings (Baylor University. Medical Center) 14.2 (2001)- 154. Link to the paper.

[3] Battles, James B., et al. “The attributes of medical event-reporting systems.” Arch Pathol Lab Med 122.3 (1998): 132-8. Link to the paper.





Analytic Graphs

An Analytic Graph is a directed labelled multigraph made and used for problem solving.

Analytic Graphs are used:
– to represent information about a problem and its solutions;
– to incrementally and iteratively design a problem and its solutions;
– to answer questions about problems and solutions that they represent.

Formally, an Analytic Graph G is the tuple G = ( N, L, A ) such that:
– N is a non-empty set of nodes;
– L is a potentially empty multiset (bag) of directed edges, that is, ordered pairs of nodes, such that (a, b) is an edge directed from node a to node b;
– A is a function which returns the label of a given node or edge;
– Every node must have at least one label;
– Every link must have exactly one label;
– There can be different types of labels.

Labels are often defined in order to enable computations over an Analytic Graph. For example, labels can be defined to enable the application of optimisation algorithms, in order to find an optimal part of a graph, which may correspond to the best solution to the problem that the overall graph describes.

Analytic Graphs came out from my research on the design of formal languages for problem solving in requirements engineering and system design. The book “The Design of Requirements Modeling Languages” (Springer, 2015) gives examples of Analytic Graphs applied to problem solving in system design.

Analytic Graphs are related to:
– multidimensional networks, in that if if only links can have labels, then an Analytic Graph becomes a multidimensional network;
– multilayer networks, if there is a partition of A, and each subgraph of G which has only the labels from a single partition is treated as a layer, and all interconnections between layers are identity relations on nodes.





The Design of Requirements Modeling Languages book

My book on how to make formalisms for problem solving in requirements engineering will be out soon at Springer. The book page is up.





What is a Requirements Problem?

You have a Requirements Problem (RP) to solve, if (i) you have information about unclear, abstract, incomplete, potentially conflicting expectations of various stakeholders and about the environment in which these expectations should be met, (ii) you know that there is presently no solution which meets these expectations, and (iii) you need to define and document a set of clear, concrete, sufficiently complete, coherent requirements, which are approved by the stakeholders as appropriately conveying their expectations, and will guide the engineering, development, release, maintenance, and improvement of the solution which will in fact meet stakeholders’ expectations.

In simpler terms, you have an RP to solve whenever you are asked by someone else to solve a problem for them, you want to solve it, and it is not clear to you or them what exactly the problem is, and how best to solve it. Situations in which RPs occur are part of the everyday, and complicated variants thereof occur often in the workplace, and especially for engineering and management professionals, although medical, legal, investment and many other professions are concerned as well.





When is a formal language slow?

A formal language is slow if it has few or no tools which were designed specifically for solving the problem at hand. Perhaps you could use that language to solve that problem, but it would take you more time to do so, than if the language already had some additional tools in it, even if these tools are simply defined from other components of that language.

So language being slow is specific to problems, or if you prefer to generalise, to problem classes.

For example, classical first-order logic is expressive, but slow if you want to use it to describe, say temporal constraints on a system. This is because it is generic, while it is fairly well know what temporal constraints look like, and why they are defined in the first place. The ontology of these constraints is known (see the modalities in linear temporal logic for instance), and first-order logic can be used to talk about temporal constraints. Linear temporal first-order logic is, then, faster than generic first-order logic when you want to specify temporal constraints.

You can easily find an expressive formal language. First- or higher-order logics for example. But it can be hard to see how to actually use that language to solve concrete problems, instances of a problem class. In such cases, you have an expressive, but slow formal language, and perhaps this is not a great position to be in.

What you need in such cases is human expertise which is applicable to the problem class, since this is what lets you understand the problem to solve. And this is what allows you to solve the problem. If you are obliged to use a slow language, this simply reflects the fact that you are facing a problem for which a strong formal language is absent.





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