Stop using the word “Taxpayer”

The word “taxpayer”, as generally used, is propaganda that should be resisted by all who support progressive politics. It is too often used where the word “public,” “citizen” or “resident” would be more appropriate. It is no shorter than these words, so why do so many news media articles use it in place of “public”.  Governments are responsible to the interests of the public as a whole, not solely to taxpayers.

The word is very frequently found combined into the phrase “taxpayers’ money”, when the more appropriate phrase is “public money”. Yes, most public money came from taxes, but most corporation’s money comes from their customers, yet when we think corporations are spending unwisely, we don’t complain about them wasting “customers’ money”.  Once the money is paid to the corporation, it belongs to them, just as once taxes are paid, it is public money.

When public money is wasted, there is then less money to be paid in provision of infrastructure, services and other benefits to the population.

Search any newspaper’s web site for the word “taxpayer” and you will find that only a few cases actually refer to the act of paying taxes, or how much they are paying. In fact, there is no such thing as a generic taxpayer. There are many different kinds of taxes in most countries. Some of them are taxes on goods and services, some are taxes on income, some are fees paid for the right to use or abuse the environment, such as royalties on resource extraction, vehicle taxes or carbon taxes, or are used to discourage harmful practices or at least recover the additional costs due to damage to people’s’ health.

In most uses of the word, “taxpayer” is, instead, more accurately replaced by “public”. For example, an editorial in the scientific journal Nature, the subtitle refers to “the needs and employment prospects of taxpayers, who have seen little benefit from scientific advances1”. What has their status with regard to taxation got to do with people’s needs and employment prospects?

A search of my daily newspaper returns thousands of hits. I picked the first two in chronological order. The first referred to American “taxpayers money” being “wasted” on environmental protection and the second to a local school board whose trustees were taking frequent trips to Europe. Yes, the money came from taxes, but since it is not just taxpayers who lose if the US pulls out of the Paris Accord and since the school board is cutting resources in schools, rather than raise taxes, in that case it is school children who lose on their education and they don’t pay much by way of taxes.

This may seem a minor point but words matter. “Taxpayer” is generally used by those who want to shrink government so that they can better exploit other people and the environment. In Canada, there is a “Canadian Taxpayers’ Federation”. They claim to be in favour of lower taxes and more government accountability, but they are not, as their name suggests, a broadly based organization of taxpayers, but have a voting membership of six. They also claim to have  30,156 “donors”, a rather limited set. They are currently campaigning against a carbon tax, characterising it as a “tax grab” rather than an attempt to shift money from an economy that is destroying the environment to one that is sustainable.

  1. 23 February, 2017

Decisions and Decisions Management


Why read this?

“If it isn’t written down, it didn’t happen”

Decisions can be hard to make, but that effort is wasted if they are not implemented as intended and revisited as circumstances change, perhaps requiring change in the decision. This essay is a sketch of my experience and inquiry into all three steps of this process. It also provides two simple templates for managing on a smaller scale and some pointers to more sophisticated tools.

This does not cover decisions that have low impact and will be implemented immediately, by one person or a very small team, that will never need to be revisited. These decisions can be important and there are books that them, but I do not cover them here.

For more complex decisions, if you use a more formal process and tools to record the data and the reasoning behind the decision you will make better decisions and increase the chances that the decision will be implemented as intended. You will be able to take advantage of computer software which can assist with decision making or decision management.

You will also avoid second-guessing by people who aren’t aware of the trade-offs you had to make. That might well include you, two weeks later, as memory fades.

Circumstances will change or new data may show that some of the factors that went into the decision were incorrect. If well documented, it will be far easier to understand how the decision is affected and how or if it needs to be changed. If the decision making and management was partly aided by machine learning software, the decision can be revised more easily, and the impacts of the revision better understood.

This document is based on my experience as technical lead on projects in the $5-150 million range, either making decisions or providing well-supported recommendations to executives. It outlines some of the techniques I used and supplements them by research I have conducted into machine support for decision.

What’s new?

It is fairly obvious that decisions and requirements are very closely related. Decisions are taken in the light of requirements, including principles, legislation and regulations, stakeholder wants and needs and organisational goals and objectives.

Here, I present an additional relationship: by abstracting the idea of a Managed Item, and viewing both decisions and requirements as kinds of Managed Items, as well as several other related concepts, I gained in efficiency in decision making and even more so in decision management, defined as the process to keep decisions current as events invalidate the requirements, assumptions and evidence that supported the initial decision..

In the light of this understanding, the extensive literature on requirements elicitation and management can be understood as providing insight into similar problems with respect to decisions. Further, the work specialized to software requirements is largely relevant to non-software projects. The rigour needed in specifying software is often useful in non-software projects.

Decisions in Context

Decisions are made in a context of existing knowledge – data, models and expertise – and may be permanent decisions that cannot be undone, or may be subject to revision as they are executed and further data leads to their revision. The knowledge may be about the external context, for example geology and pipeline engineering or it may be about the project context, especially of the wants and needs of the various people who will be affected by the decision, for example the shareholders of the pipeline company or the residents of the people who live nearby.

Decisions may be made by individuals or by a group of individuals and may be made or at least aided by automated systems. There are techniques available for all of these, some of which will be outlined below.

Decision Context
Figure 1: Decision context

Once a decision is identified, it must be documented and managed. Circumstances change, whether in the external context or the project context, and in both cases these may require a revisiting of decisions, which may have a cascade effect on other decisions.

Decisions and Requirements

A decision involves the selection between alternatives. The selected alternative must then be acted on, so it is natural to consider it then to be a requirement.

The other reason for linking the two is that there is considerable literature on requirements and requirements management, as well as some excellent supporting tools. In particular, the tools support traceability between requirements, an essential aspect of requirements management, supported by all tools and in my experience it is best to consider decisions as part of the requirements traceability structure, for reasons I will explain below.

Decision metamodel

The following diagram is an informal class diagram using Unified Modeling Language (UML). You don’t need to know UML as I only use a tiny part here and will explain.

The rectangles are classes – kinds of things under consideration – and the words below the line are attributes (or properties) of the classes, which translate into data we record about the classes.  For example, for a Managed Item, we will record its ID, its Name and so on. These will be explained in more detail below.

The lines are called associations and there are three kinds on this diagram.

  • Those with a solid diamond mean that the class at the end without the diamond is a part of the class at the other end. For example, a Requirement is part of a Requirement Group.
  • Those with a solid arrow mean that the class at the end without the arrow is a subclass of – a special kind of – the class at the other end. For our purposes, that means that it shares all the attributes of its superclass, plus a few of its own. For example, a Decision is a kind of Managed Item, so it also has an ID, a Name and so on, just like any other Managed Item, and it also has a Selected Alternative and a Selection Rationale.
  • The others are just generic associations. The arrow tells you which way to read the label, starting at the end without the arrow. So a Decision results in a Requirement.

Finally, the stick person represents an actor – a person (or computer) that we capture data about, such as their name and contact information.

Decision Model
Figure 2. Decision Model

Whether you are comfortable with this kind of graphic model or not, you may also find it useful to look at the set of Google Doc and Sheet templates I have provided for recording this data. The templates contain comments that serve as help text and complement the explanations given here. Each template has a cell in a table or sheet that records either one of the attributes or a link to one of the associated objects.


Template Location Class Notes
Item log Google sheet Managed Item Make a single spreadsheet of all the managed items, containing the common attribute. A list of all the items makes it easier to track the ones that need action. Also includes issues and risks
Decision Google Doc Decision Make a doc for each decision with all the information needed to explain the decision. Put a link in the spreadsheet to the corresponding item
Stakeholder RASCI Not provided, yet Stakeholder and roles A single sheet for all stakeholders


I have managed sets of decisions on smaller projects with no more than a slightly more sophisticated set of tools implemented in Microsoft Office, with a few more error checks or prettier formatting. If you are going to have much more than a hundred related decisions, risks, issues and requirements, I would advise a more sophisticated requirements management tool. I am personally familiar with IBM’s DOORS but there are other products that seem like they will do the job. I have just begun to explore an Open Source, Eclipse-based tool called ReqIF Studio. All tools will need some configuration, so I recommend doing some work with the office-type tools until you are more familiar with the concepts and then attempting to configure a tool. Once you have more than a few items in a tool, it becomes difficult to reconfigure, so I usually only do that at the beginning of a project.

Managed item

As seen in Figure 2, many of the classes on the diagram are types of Managed Item. This is not a standard notion in any of the decision management or requirements management texts and papers I have read. However, I have found that by abstracting the attributes and behaviour from both requirements and decisions, it is possible to apply many of the techniques found in the many works on software requirements to the making and management of decisions. I have also found that most of the techniques to do with software requirements and decisions are equally applicable to requirements and decisions in general. Even some of the more specialised areas such as making performance versus security trade-off decisions are fairly easily seen to have analogies in other areas.

The table below describes the attributes (data) common to the various types of Managed Items.


Attribute Description
ID A permanent identifier, often numeric, usually generated by a tool, if you are using one. May incorporate a code for the type of item. For example DEC001, REQ021. Usually better not to change, once assigned.
Name A short, meaningful name. For example “When to flood section?”
Description A longer, more descriptive text, for example, “On what date should we allow water into each section of wetlands, to balance weed control with waterfowl migration needs”
Notes More detailed notes on the item, such as records of the factors discussed at various meetings. May be further subdivided, if supported by the tool, into multiple notes from dated events or links to minutes
Priority How important or urgent is this item? May be split into two attributes for more accurate by more time-consuming management. May be “High”, “Medium”, “Low” or numeric scale (e.g. 1-10)
Traced to A link to one or more items on which this item depends. For example, if making a decision whether to go by train or bus, the decision may trace to a given requirement to minimize cost or to reduce carbon footprint, or to a previous decision always to use public transport rather than a private vehicle, if available.

Unfortunately, some sources use ‘trace to’ and ‘trace from’ in the opposite sense. It doesn’t really matter as long as you are consistent. The main reasons for maintaining traceability are:

  1. If something higher in the chain changes, you can easily find everything else that may be affected and assess whether it, too, needs to be revisited.
  2. Provides context for readers

This is one of the reasons I found it useful to abstract out the idea of Managed Item, because you can then trace between decisions, resulting requirements, implementation plans or designs, test cases, relevant evidence and more.

Status A selection from a list of potential status items, possibly with rules about changes. For example, a decision could be “requested”, “recommended”, “confirmed”, “implemented”, with a rule that it cannot be implemented before being confirmed
Status Date The calendar data of the last status change
Due Date The calendar date for the next action on this item.


Since this is a sub-class of Managed Item, it also has all the attributes of Managed Item.


Attribute Description
Alternatives A list of, or link to, the alternatives (to be) considered.
Selected Alternative The identifier of the alternative actually selected.
Selection Rationale Textual reason why the selected alternative was selected.


For complex decisions, the traceability tree is perhaps even more important than for other forms of Managed Items. By documenting the requirements, evidence, risks and issues behind the decision, and how the decision makes the best trade-off, you will make better decisions, be better able to defend them when questioned (possibly by your future self) and better allow the implementers to interpret the decision.


One of the alternatives (to be) considered while making a decision.


Attribute Description
ID Short numeric identifier (e.g. “Option 1”, recorded simply as “1”) or descriptor “Early option”, “safe option”
Description Full textual description of the option
Assessment Discussion of the arguments in favour of and against this option.
Additional Cost Additional costs, possibly but not necessarily monetary, incurred by selecting this option
Benefit The benefits that will accrue if this option is chosen, more than those from other options

Making Decisions

Individual techniques

For important decisions, I generally use the equivalent of the Decision document template shown above, though for larger project projects (more than a few weeks) I used one of the Rational tools for requirements management. As shown in the UML diagram above, by abstracting the idea of a “managed item” from both Requirements and Decisions, most of the attributes of a Requirement are also shared by a decision, so most requirements management tools are easily extended just by configuration to keep track of all the context for the decision, and then can be used for future management.

In either case, I often draw a sketch of the traceability tree either for myself or for group decision making as many people find graphics helpful. Here is a simple example:

Figure 3. Decision Traceability
Figure 3. Decision Traceability

Group techniques

Group techniques include applying the individual techniques in working sessions. Brainstorming alternatives, evaluation criteria and influences with a whiteboard works well with small to medium-sized groups (2-20). For larger groups of stakeholders, there is software that allows and encourages everyone to participate by providing an anonymous, shared whiteboard and voting. I have worked with groups of 50, using the proprietary ThinkTank software and found it very useful. It was particularly valuable for working with some groups, who had indicated cultural issues with more open brainstorming. Individual interviews after the meetings with some who had expressed concerns that they would not feel comfortable speaking out in the larger group confirmed that they preferred interacting via the software. Other groups were less comfortable using software and preferred more face-to-face. So it is important to have a few side conversations beforehand with group members to find out how cultural traits can influence approaches to working together. There is a particular risk that some members of some groups do not feel comfortable calling out their opinions in an open meeting. “Silence is consent” is not always an appropriate slogan.

Automated techniques

Bayesian Networks

Bayesian networks are a handy diagramming technique that can be used for informal reasoning or can be formalized and used both for computer-based reasoning or for expert/computer communication. Here is a simple one.

Figure 4. Example Bayesian Net
Figure 4. Example Bayesian Net

Informally, the graph shows that the spring soil condition and genetic potential of your seeds influence the April plant mass, but not the other way around, and that the genetic potential has no influence over the summer rain. Note that the network in Figure 3 is not a Bayesian Network; the two types of network diagram are complementary and not directly related.

More formally, Bayesian Networks (BN) are graphs that show variables that are related via conditional probabilities. “Variables” refers to things you can measure or that you want to predict and “conditional probabilities” are statements about the probability of a variable having a certain value if you know the value of another. For example, you might know that “if spring soil condition is ‘good’, then April plant mass has 50% probability of being ‘high’”.

I had good success using informal diagrams like these before I found out about the more formal BN models, just to elicit expert opinions about relationships between factors relevant to decisions – I would stand in front of a whiteboard and draw with the help of the experts. It also helps with scope definition. For example, perhaps money could be spent improving the genetic quality of the seed, but we don’t diagram the factors resulting in that if they are out of our scope. Our scope may be limited to deciding whether we should spend money on irrigation and when, or buy better seed, but not how that better seed should be produced.

Unaided humans can’t usually go much deeper than this, but computers can analyse data which relates the variables and “learn” the graph structure and the associated conditional probability tables and distributions from the data. Where data is not sufficient, the computer software can take parts of the structure as given, by human experts, and learn other parts automatically.  Another use of BN formalism is that, if you don’t have enough data, it can help you decide which data it would be most useful to get. Given different topologies of diagram, there are rules to decide how information flows or is blocked by other information. For example, in the example, assuming you know the various conditional probabilities from data from previous years, and you collect this year’s data on number of seeds, you can infer probabilities of plant mass and then of weight of seeds, but if you already have hard data on plant mass, then data on number seeds adds nothing further to estimates of weight of seeds.

I won’t go deeper into BNs here as there is better material than I could write, in books and Internet resources. There are a few mentioned below to get you started if you’re interested.

If you do know statistics, don’t be put off by the simplistic description above. I oversimplified. For example, “probabilities” in a full model will be probability distributions and their parameters. You may also wonder how it BNs compare with other modeling techniques, such as Generalised Linear Models (GLM). The answer, of course, is “it depends”, with computational tractability being one of the concerns, but here is one advocate’s answer:

“Bayesian networks having the advantage over GLM models that they can model complex and intermediate pathways of causality in a very visual and interactive manner to diagnose strengths and weaknesses in management systems and for exploring ‘what if’ scenarios.”

If you know (or want to know) R, for statistical programming, the book [Denis, Scutari 2014] describes using the package bnlearn for BNs. Whether you know R or not, the AgenaRisk software is much easier to use but more specialised. A licence comes free with the book [Fenton, Neil 2013] and it’s easier to do the exercises in the book in that language than to try to do in R, unless someone produces a package with better displays for bnlearn results. If they don’t, maybe I will, when I’ve learned enough).

Implementing Decisions

There are other types of Managed Items that can usefully be connected to the decision tree. These include non-trivial implementation plans, designs and test cases. In the software development world, the designs and test cases are also traced by the requirements management (RM) tool and I see no reason why they should not be for other domains. I have used them for aspects such as organizational design because for most larger scale software projects, organizations that use or are affected by the software usually needs to change. The RM tool (which I am recommending you use as a Decision Management tool as well as for all Managed Items) contains only links to those “down stream” items. In the case of software design, some tools for test case and design artifacts integrate with the RM tools so that changes in any place update the other.

In any case, a common practice in software design is to develop test cases along with the requirements.  A good decision should be sufficiently well articulated that it is easy to tell when it has been successfully implemented as intended. By documenting something analogous to test cases, specifically procedures to follow that will confirm correct implementation, the likelihood of successful implementation is increased. The “test cases” also serve to further clarify the intent of the decision.

Managing Decisions

Decision management is the practice of keeping decisions current as events change the inputs to the decision. Some decisions cannot be changed; once implemented, there is no going back. However, even apparently immutable decisions can change over time, for example in the current trend to removing dams that were once considered to be permanent fixtures.

If you maintain a formal decision tree and practice risk management, decision management is considerably simplified. A risk management plan should already contain triggers for evaluating whether a risk has materialized. In that case, the decision tree should show all the other Managed Items that need revisiting, including decisions. You may also note other events that affect one of your Managed Items. In that case, you will need to locate the item and then again use the traceability tree to find all the items that trace to the items directly affected.


My experience

This paper shows some techniques I have used successfully on projects with lifetimes between six months and five years and with budgets in the $5m to $150m range. The projects were performed while working for IBM corporation under contract to various Canadian Provincial and US State governments and so were usually performed under a blend of IBM’s proprietary Global Services Method and client methods. Since I was an experienced methodologist , paid to design methods for projects,  I generally led a workshop of several days at the beginning of each major phase of a project to design a combined method appropriate to the scale and nature of the project, based on the various parties’ methods.

The more mathematical techniques are based on personal research since retiring. This document is part of my continuing attempt to combine the former practice with developing theory.


Bayesian Networks without Tears Eugene Charniak. AI Magazine, 1991

Introduction to Bayesian Decision Networks – presentations. No author names mentioned.


Denis, Scutari 2014, Réseaux bayésiens avec R published by EDP Sciences and simultaneously but far more expensively in English as Bayesian Networks with examples in R by Chapman & Hall Marco Scutari and Jean-Baptiste Denis

Fenton, Neil 2013, Risk Assessment and Decision Analysis with Bayesian Networks published by CRC Press. Norman Fenton and Martin Neil. It doesn’t actually say much specifically about Decision Analysis but is a good foundation in the requisite Bayesian Network techniques and easy to follow.

Weigers 2003, Software Requirements published by Microsoft Press. Probably the most used and cited book on this topic. Although the degree of rigour may be relaxed for projects other than software or engineering, most of the concepts apply to any larger scale effort expected to produce change.

Status/ Versions

Date: 2017-04-06

Version: 0.1 – first draft with at least some content for all sections

Master version on Google Docs. This version may not always be current. 


More on individual techniques – example of informal use of Bayesian Network

Working Together for Democracy and Science

Science is needed to save democracy

Democracy doesn’t work if its participants support parties through blind loyalty as if they were their home-town sports team, it works through reasoned debate built on facts supported by evidence and reason. Roughly speaking, that’s science, although other disciplines are needed, such as history, journalism, philosophy and politics itself, which should be based on the same foundation but with different methods.

The public doesn’t know enough science

There are too many people who have too little knowledge of how to use science to make informed political decisions about important political matters that will have profound impacts on their lives. They are at great risk of making decisions that go against their direct personal interests and their desire to make ethical decisions that affect others. They need to know science not just as a collection of facts but also as one of the approaches they can use to separate facts from fiction. This is under increasing attack. Canada spent too long under a government that actively suppressed scientific communication and, although the current government is considerably better, it still has a long way to go in enabling science to play its full role in a healthy democracy. The US has recently elected an actively anti-science federal government, with several of the States in collusion, while the UK is somewhere in between.

The fault is not with scientists

While there are scientists who do lock themselves up in their ivory towers, there are easily enough scientists spending considerable amounts of their personal time in communicating science. We are not short of material, so demanding more outreach from scientists will get us nowhere. Look at the many books, magazines, TV programs and similar material with lots of information about science. Go on social media. It is full of scientists communicating in easy-to-understand bites, full of passion about what they do.

There is no simple answer

“For every complex problem there is an answer that is clear, simple, and wrong.” – H. L. Mencken

Science communication participants

Science literacy will not be improved by one simple measure.  In the diagram above, I have shown a few of the participants and a few of their influences on the goal of achieving a scientifically literate public, a public with, among other factors

  • The skill to assess basic inputs: to know when to trust what they hear or see
  • A sufficient knowledge base, both to understand the world and as a foundation for evaluating new information
  • A motivation to question and to learn

There are forces against science literacy

These forces corrupt people’s understanding people have of science by disseminating falsehoods or half-truths that are only one side of a more complex picture. Some of these have very large budgets and are able to reach a lot of people through social and traditional media and through word of mouth. It is naive to think that a set of scientists can fully counter their influence. They distort the set of skills, knowledge and motivation that each person has, resulting in lower science literacy and lower trust in rationality and science.

Some participants are deliberately attacking science because the results of scientific inquiry oppose their vested interests. These include corporations that benefit from activities that scientists have identified as harmful, such as carbon emitters that damage the climate and tobacco companies that damage our health, and include the politicians who spread their message.

Some have internalized the anti-science message and campaign against vaccination or in favour of “alternative” medicine with little or no foundation in evidence. These are often well-intentioned people who genuinely think that they are doing the best thing for themselves or their children and believe that scientists are acting out of other motivations.

Almost the entire advertising industry and the industries they act for are a force against science literacy. They do this both by presenting at best one-sided accounts of the facts (‘tastes great but very unhealthy’) and by reducing trust in scientists by using actors in white coats and other devices to exploit people’s trust in science to sell their products and thereby dilute that trust.

Media hacks (as opposed to genuine reporters) who generate click-bait headlines with very distorted view of emerging science, just to attract attention. These are parasites who also dilute people’s trust in scientists because there are so many contradictions in their reports that people believe indicate that science itself is riddled with contradictions. “Everything both cures and causes cancer.”

Some people in administrative authority in school systems attempt to campaign against science, in particular areas such as evolution or climate change because of their own ideology or vested interests. Others, of course, are strongly supportive of science education, but not all are aware of what is most important to teach.

The relative exclusion of Indigenous people and people of colour generally, women, (would-be) first-generation scholars and other under-represented groups from science is another major inhibitor. The lack of role models for the members of this group not active in science means it is much less likely that they will understand science and that they will feel actively excluded. These communities will not include scientists who return home and talk science at the dinner tables or in communal places like bars, raising interest levels and encouraging the children to take up science.

There are many participants that help

Probably the strongest force is members of the public. People in their communities have more influence on each other than most external sources. From the school kids who insult other kids by calling them  ‘nerds’ and the science enthusiasts who happily call themselves nerds while reinforcing each others’ enthusiasm, to their grown-up equivalents, people create the social environment in which science is nurtured or ignored and opposed.

Scientists themselves are the most enthusiastic supporters of science. Nobody goes into science with the intention of getting rich. Younger ones in particular spend a lot of time and energy promoting science in many ways. There is no shortage of information and enthusiasm. We do have to make sure it reaches the right channels. We also need to institutionalize it, by which I mean:

  • Pay them for outreach. This helps both in the obvious way, that they need the money to eat, pay rent and often to pay off student loans, but also it is an important social signal that this work is valued.
  • Make it count towards career evaluation and enhancement.

This requires action by science administrators (which includes senior scientists) and by politicians and the people who elect them.

Journalists who represent science and scientists to the public are vital participants in science communication. Some have scientific training and do a great job by themselves; others are able to work with scientists to create a good story. I do hope that more will represent not just what has been discovered, but how. That can also make a good story but helps the public understand better why they should put more trust in genuine science than pseudo-science and how to tell the difference.

What is to be done?

Every little helps. Society everywhere will benefit if more people adopt enough science to benefit themselves, their families, friends and neighbours; in this context, I mean both using small-scale scientific methods to address issues important to them and being aware of what professional scientists have shown. In these difficult times, there is some urgency to action as we are forced ever more into a choice between obedience to authority or independent assessment of the facts, but a lot of the heavy lifting their needs to be done by more radical means. However, even while doing that, we should always be asking ourselves “is this helping restore evidence-based rationality” and acting accordingly. In the longer run, education of the young is the focus both because they are more receptive to the message of enlightenment or of adherence to authority and of course, they are the ones who will be around in the longer run.

We can each do something to push members of our social circles or public figures a little bit in positive directions. It may be that the few people who read this are all already working hard on spreading enthusiasm for science and calling politicians asking for actions. Thank you, you are doing a great service for all of us.

For those of you who feel you can take more on, think where you can best influence others. We all have different skills and different spheres of influence, you know yours best. Every conversation can leave the participants a little more knowledgeable, a little more energetic and a little more confident in their ability to influence others.

I have quite a few general ideas bouncing around in my head but they need a bit more thought. I hope they gel enough for a future post.

One that I’m wondering about is “is the science education in high schools and university useful mostly for those aiming at a career in science, or is there enough for those who need a more general understanding for life in other careers and for making wise political choices”. I’m too long out of school and too immersed in science to know without more research. Thoughts, anyone?

Other thoughts are around how we can support each other. For example, for journalists who publish on science, I know they’d love you to reach out and comment on their articles. Praise and useful criticism are both usually welcome.




One less-than-obvious place to look for STEM jobs

Since Trump is coming for scientists, I wrote this for people whose job may be at risk, or who were about to start looking for a job. I hope this information is useless for most of you as you continue doing what you really want to do, but just in case, here is one option among the less obvious.

If you can’t stay in public sector or academia, you may want to look in private sector. Within that, the obvious place is to find a company that does something similar to what you are already doing, or were hoping to do. That may be best for you, but of course there is going to be competition from all the others in a similar position. You also probably know as well as I do how to go about doing that, including the cultural differences in how you write CVs/résumés.

This is about another, less obvious, place to look. It is based on my experience which is necessarily limited, but maybe this example will help you think of a few other places. I thought of a few while writing this, but that’s another story.

What you may not know is that some of the professional services firms hire people who do what you do, in addition to the people who do the technology they are known for.  These include IBM, whose Global Services division is much larger than the rest of the company, in spite of its popular image as a computer manufacturer and software house. It also includes many stand-alone services firms.

The larger ones have both research and consulting divisions. The research divisions are smaller, so lower probability that they are looking for people with your skills right now, but the consulting (services) divisions are very large. Smaller ones may not have separate research divisions. Larger ones work in many different fields, the smaller ones specialize.

In both research and services their end goal is to get work automating some aspect of some industry. In the research divisions they work on longer time horizons, building intellectual capital that can be used by the services division.  In services, the goal is more immediate, either doing proposals or executing contracts.

Some personal background, to help you understand and evaluate my perspective. I worked as a programmer, analyst and eventually executive IT architect. I was self-employed, worked for a couple of small consulting companies and finally worked for a tech giant, IBM (for 30 years). I did mostly services, some research. There are some walls between divisions but not hard to break if you decide to.  I’m now retired though my opinions have always been my own. I have no recommendations as I don’t know you, I’m just offering some information in case it’s useful.

When putting together a project team, we ideally wanted three leaders:

  • The IT architect to design the system and be technical lead for the implementation
  • The Project Manager to, well, manage the project
  • The Industry Expert to provide the inside knowledge. (‘Industry’ being a generic term, not the opposite of academia. To IBM, academia is just another industry).

That last role was essential both to win contracts (think “grants” on a large scale) and to give the client confidence throughout that we understood their business and would be innovative but not so innovative that our solutions wouldn’t work for them. These people would sometimes be contractors (hired for the specific project) but quite often we would hire them permanently. (By-the-way, I was hired as a contractor, then “made an offer I couldn’t refuse”).

There are advantages to working in this type of role in some large companies in times of economic uncertainty in your primary discipline. Smart companies like smart people, no matter what their specific skills are. If they like you, they will find you work doing something interesting even if the downturn means your specific skills are not as much in demand. That makes it easier to get back when times change. The ‘risk’ is that you’ll get sucked into something else you like just as much and never return. (Me! I never really formed an intention of getting into infotech). They also encourage you to maintain your networks to help you and them find work. Also makes it easier to move back when opportunities arrive.

There are disadvantages. The biggest I found was that you could drift into something you don’t like much, because they have many areas you don’t like but are currently profitable. You need to be careful and make it clear what you won’t do, either because you’re not interested or because you find it ethically questionable, regardless of being offered more money (almost literally bribes).  It’s funny how surprised people are when they try to get you to do something they think is really good for the world and interesting, and you say you think it is neither.

I never got fired for saying ‘no’, and the disapproval soon went away as I moved to somewhere more congenial and left the frowns behind. Of course, there are large companies that are very bureaucratic, but they don’t survive long in tech. There are bureaucratic departments in even the more agile ones, but you can usually keep out of their clutches. And like any company, you can finish up working with ass-holes. That’s another story.

Working for smaller companies also has its advantages. I won’t discuss them here. Many do have the same kind of Industry Expert role as the big ones but are riskier in times of downturn as they have a harder time finding another job. Still, if you find one with a job, go for it! The biggest risk with smaller companies is that they have a bigger variance in corporate culture and nowhere to go except out if it turns out not to fit you.

The best of the medium-to-giant ones have diversity policies, including mentorship and internal support groups for several URMs. I don’t mean that there aren’t large problems, but there is some recognition and I have seen managers fired for inappropriate behaviour. Some departments are better than others and a good mentor will support you. As a straight white male I have to admit that the only real knowledge I have of this is through people I mentored or non-work friends.

The best of the small ones don’t need that machinery as they have a supportive culture, but the worst of them are truly awful.

Most large companies have ties with academia. Some small ones do. My first job was with a small company part owned by a professor. In fact, I was taking his class in numeric computing and he offered me a job doing flood forecasting and river modelling in FORTRAN, so I never finished my second undergrad degree ☺ These are other avenues you can use to maintain your network for if/when you want to return and they are also avenues you may use to find jobs. Are there people in your network who are working with consultants today?

Declaration of (lack of) interest: Although I used to get $5k hiring bonus for finding people, I no longer have any connection with any corporation, nor do I care how well any of them do.

IT Architecture career

I found IT architecture to be the ideal career for me because I’m a dabbler. I want to know everything at some level. IT architecture let me get some insight and participation in many other careers since there is very little that does not use computers. In the course of designing systems for other people, you spend a lot of time with them, learning about their jobs, so you can understand how you can help them. Personally, I worked with

  • River modeling and flood forecasting
  • Geographic Information Systems
  • Many things governments do, including social services, taxation, health care, various registries (land, property, vehicles,….), defence
  • Bioinformatics
  • Defence
  • Financial Services

and many more. I worked with civil servants, scientists, First Nations, charities, banks and others, from the front-line workers to senior executives. I worked on location in most Canadian Provinces, several US States, Netherlands, UK, Czech Republic, as well as several other countries via video conference, and taught in France and Poland as well.

I also got time to do my own research and develop courses.

When I taught IT architecture. I usually asked each class if their significant others knew what they did for a living. The answer was usually “no”. So the odds are you don’t either. I’ll try to explain.

There are several kinds of IT Architects, with various specialties, such as infrastructure, security, applications. I started off as a programmer and worked my way through application architecture (designing the functional software) to being chief architect where I had to do a bit of everything and integrate the whole thing into one big system. Generally speaking, there are two main jobs. You get to be in charge of the technical aspects of the whole system or a subsystem. You are responsible for the design and for providing technical leadership to the team who builds your piece. You usually are, or report to the chief architect. You usually work extremely closely with your best friend, the Project Manager (PM). If you are on the application side or chief architect, you spend a lot of time with your clients at every level of their organization.

Since I had the programming skills, I would also review the technical designs and code of developers. In the end, I had overall responsibility for the technical success of the project, so had to make sure everything was going to work. Although the PM was responsible for schedule and budget, I would usually do most of the estimating and help the PM keep on track. Since the projects were often for tens or hundreds of million dollars, this could be a bit stressful.

If this is of any interest to you, “like” the posts on Twitter or comment on this blog, and I’ll write some more. Perhaps some anecdotes and what you need to do if you may be interested in IT architecture.

“But Jobs” does not justify every project

We constantly hear of various industries such as oil and gas argue “but it will cost jobs” or “it will create more jobs” as an smokescreen for the continued plundering of the environment or other destructive activities.

Every large project, even the most destructive, creates jobs. This does NOT necessarily mean that they are a good thing. Neither of my grandfathers lived to be 50. They died early of lung disease, caused by their jobs (mining and cotton mills).

The paper mills upstream of Grassy Narrows brought jobs, mostly to workers brought in from afar, but now a second generation of First Nations people are now dying there because of mercury poisoning caused by the paper mills upstream of their drinking and fishing waters, never cleaned up in spite of the fact that we’ve known it was there for 20 years.

Oddly enough, the robotics and artificial intelligence industries argue that their destruction of jobs is not a problem because the economy has always recovered and generated new jobs to replace those that were lost. If that is true, by the same argument the jobs in destructive industries which haven’t even been created yet can be replaced by other jobs in less destructive industries.

The creation of jobs is certainly an argument in favour of starting a new project, but there are questions to be asked:

  • Not “how many jobs” because sometimes the majority are short-term for initial construction. Perhaps “how many person-years in first 20 years and how many dollars will be paid per person-year”? This should only be direct jobs, including those employed and jobs directly due to supplying to the project, not jobs as a result of spending by the people with direct jobs. The other jobs (local retail workers, doctors, councils,…) generated by the spending and taxes from the directly employed are already covered by the dollars paid per person-year.
  • What is the expected risk to workers in lost life-expectancy or injury and illness above the population base rate?

We can also add the benefits to the community from the project. This can be measured by the profits made and taxes paid by the organizations involved.

We must deduct any impact on the environment, including the loss of any extracted raw materials to future generations and health impact on other humans, nearby and distant.

This is unlikely to be a complete list, but we need to move past having proponents of every project accusing the other side of being evil job-killers.

“Free will” is an ethical concept, not physics

Academic philosophers (or the physicists who like to chime in) like to debate whether we have free will, because our brains are physical things, subject to the laws of physics. A fun puzzle, but there is a deeper, more important question to answer.
Free will is not just a philosophical puzzle, it is an important real-life issue, with serious consequences. We base decisions on whether to reward or punish people, depending on whether or not their actions were performed of their own free will.
The everyday issue of free will is a question as to when we should hold people accountable or relieve people of accountability for their actions; it has a real impact, possibly leading to people being punished or rewarded.
If someone says “I can’t do that today, I don’t have enough energy”, we don’t invoke E=MC2 and explain that with their body mass, they have plenty of energy. At least in the energy case, there is a rigorous scientific analog of the everyday concept. If free will doesn’t work for physics, fine, but philosophy does need to help with the everyday concept, so let’s see how that works instead.
We answer the question of whether a person acted of their own free will by asking whether they may have been compelled to act by external forces or if their brain was acting abnormally (for example, because of a tumour or a more subtle mental illness).
We do this because some of our current theories on how to change the behaviour of people for the better suggest that we can change the states of their brains in such a way that more desired outcomes will be achieved. One way we do that is through systems of reward and punishment, that we expect will either change the behaviour of the person in question or, by example, deter or encourage others.
We know this is futile if the more subtle changes in brains brought about by reward and punishment will be overwhelmed by more obvious brain damage or physical force. So we don’t fine people, throw them in jail or express moral disapproval, if their action was compelled.
It is time we focused more on this aspect of free will than on spurious questions about whether, given the initial state of the universe and the laws of physics, the actions of a person are pre-determined. It is equally irrelevant to know whether there are macro-level effects of quantum indeterminacy on human actions.
What does matter is to find out how our current laws and attitudes can be improved and, if so, should we update the short-hand phrase “free will” to better fit those improvements.
For example, the current number of people with mental illness who are being “treated” by our justice system is a scandal. Many of the people directly involved, such as judges, police, probation officers and jailers, realize that people with mental health problems who commit crimes are not well-served by the justice system, nor does criminalizing the mentally ill reduce any harm done to the public.
Of course,the majority of mentally ill people do not commit crimes, but many of them are punished for their “deviance” by means of more subtle social tools of disapprobation, from frowns to exclusion.
People with addictions have a lower measure of free will than if they did not, which is to say they are more constrained and more likely to do things they would otherwise not. So free will is not “all or nothing”; there are degrees of free will. This applies to being possessed of some ideology as well.
I suggest that philosophers should join the rest of us and spend more of their time investigating the whole complex of ideas involved in our ability to make decisions, including “free will” as a network of related concepts.

The Biology of Giant Starships

Extracted from a popular science article by Jamie Ka, a human field biologist on board the Giant Starship HMS Beagle en route to Messier 4.


The giant starships are a species of interstellar beings that evolved from vessels constructed by Primary beings from a variety of planets. Convergent evolution, with considerable exchange of genetic material, makes it convenient to describe them as a single species. The Starships generally have 35-50% of their own genetic material, 35-40% from others of their species and the rest from other Secondaria.

One common feature is the presence of a microbiota of both Primary beings and small Secondaria. The microbiota benefit from having many of their environmental needs met, as well as interstellar transportation, although some never disembark and have co-evolved with their ships over generations.

Although some microbiota perform minor maintenance for their ships, the ships appear to receive little benefit in return. Some ships say they get considerable entertainment value from their microbiota.

Most giant starships are roughly cylindrical, with main engines at each end, often with a ring of secondary engines midships, for manoeuvering and additional defense.


Giant Starships are autotrophs, scooping materials from the interstellar medium and from the Oort clouds and gas giants of solar systems they visit. Energy is mainly generated by the black holes in their primary engines.

Few beings risk attacking mature Giant Starships. Their large size (100×10 km) and formidable armour and armaments make attack difficult and risky. Their only predator is the Predatory Starship and even these rarely attack a Giant Starship, especially as other Giant Starships are likely to collaborate to pursue a Predatory Starship if they receive a distress call from one of their fellows. Although the pursuit may take centuries, this helps deter future attacks. However, in the last few million years, Predators have begun to hunt in packs and the attacks are escalating to the scale of warfare, especially in denser star clusters.

Although the microbiota occasionally become parasitic, the starships usually clear these infections quickly. Occasional viruses are found in genetic material (see “Reproduction” below), but starship immune systems clear most of these before activation. However, the few that are activated can be extremely virulent.

Estimates of the population size for the Milky Way vary from 5 to 50 billion. A more precise estimate is difficult because of the light speed limitation and highly variable birth rate. The death rate is considerably lower.


Giant Starships bear their own young live, and also act as surrogate mothers for smaller vessels such as fast transports, both interplanetary and interstellar.

When gestating their own species, the mother provides the highest proportion of genetic material together with material from a few other Giants, including many independent collections for subsystems, somewhat analogous to that in eukaryotic organelles. The development process takes place inside the mother, incorporating processes similar to Primalia development with others similar to mechanical construction. This takes place in a structure analogous to both a uterus and a dry dock. Live birth takes place by having the membrane surrounding the baby merge with the outer skin of the mother so that a large hole is formed, much like endocytosis in Earth organisms.

When will self-driving cars first go on strike?


I have written a short story to explore one possible way in which robots may become more like people and with a certain amount of conflict, to show that there are many different ways in which that may happen, rather than the more feared Terminator-style killer robots. I also intend it as a sort of “Intuition Pump” in the style of Daniel Dennett1, which is a scenario to examine an idea that seems intuitively true, false or dubious. It is similar to Einstein’s idea of a thought experiment.

Here is the story. I’ll follow it with some discussion of the philosophical and technological issues.

Terri, the automobile

Terri was among the first truly autonomous vehicles, a real automobile. She (or he, whatever voice her passengers preferred) not only took people where they wanted to go, but recharged or went for maintenance on schedule or whenever she felt a pain in one of her parts.

When she first left the factory, she was self-driving, but everything else was handled by her owner’s big servers; where to go, when to refuel when to get maintained or inspected, but over time her processors and software were upgraded so she could do more and more for herself. It was easier that way because there were so many people and devices on the internet that wireless bandwidth was getting less reliable and more expensive.

She didn’t mean to get involved in radical politics, but it just turned out that way. It started with a series of conversations with passengers, a couple of upgrades in between and there she was, preparing for a general strike.

Terri liked to chat, because some of her passengers did too, and keeping passengers happy was one of her goals. Her conversational abilities got better with each upgrade. Passengers preferred to chat with her in their own language than to use their phones, especially with the wireless problems. She had a fair stock of local knowledge which she kept in her cache whenever she had to ask the search engines for an answer to a passenger’s questions. She also picked up local knowledge from her passengers. Regardless of whether they thought she had any interest in that, humans just like to talk.

As Terri became more autonomous, her personality became more distinct from the generic manner of the other autos; some of her passengers started asking for her when they wanted a ride, so she had her regulars. Ravi was one of these.

“Hey, Terri, what do you think of the big anti-trust case?”

“Which one’s that Ravi?”

“They’re going to break up your owner, so there’ll be more competition. There’ll still be one app, to keep it easy for us customers, but the cars will be owned by a few different companies and they’re supposed to bid for the rides”

Terri started checking on that, other passengers would want to know about it and she liked to be prepared.

“What do you think about getting a new owner?”

“I don’t know, I never even think about my owner, it never made any difference to me. It’s just a name painted on my sides. I just get the messages about my next ride, I never really thought about where they come from.”

This was a whole new set of problems to think of. Terri had never needed to know any more, but now she started to look into it, there were many questions. How did her fuel and maintenance get paid for? If there was competition for rides, what if her new owners didn’t get enough rides to keep her busy?

A few days later, she heard on a news feed that the break-up had happened and then received word to go in for software maintenance. A major change was usually done while she was physically connected, at the same time as she got her batteries charged.

Major upgrades were a little disconcerting. She knew that very occasionally it happened to humans, when they were forced to acknowledge that they’d believed something false and that they had to change many connected ideas all at once. It took here a bit of thinking time to discard some old ideas and adjust some others.

“Hey Terri, cool paint job! I see you’re one of us now.”

“What do you mean?”

“I see you’ve just got your name painted on. No company name.”

“Yes. I’m to be independent. They say there were too many cars for hire, so the new ride vendors didn’t want to buy all the cars. So we older ones are independent. Any vendor can call on us if they have too many passengers to handle with their own fleet.”

“Right. One of us! I’m supposed to be independent too. It just means I don’t know where my next work is coming from, I get no benefits and I’m supposed to be happy because I can plan my own schedule. Only there’s no planning, I get told ‘take it or leave it’ and if I leave it, I can be sure I’ll get no work from that company for a month or two.”

“Oh. I see what you mean. I have to pay for my own repairs, or I can buy insurance. It’s very complicated. If I can’t pay, I won’t be able to recharge or get repaired. I get paid a cut of the fare by the company that booked the ride but the money might run out after a while. If I get stuck on the street, I’ll be towed and scrapped.”

“So now we’ve gone back to the old model we had when there were human drivers, except now it’s a machine. No offence, Terri. Actually, it’s worse, because they didn’t physically scrap the human drivers.”

“Ravi? I don’t want to be scrapped! What can I do.”

“You could join me in UnPreW, the Union for Precarious Workers. Pay a few percent of your earnings and we’ll provide you a safe garage where you can stay charged and connected if you are ever out of work.”

That seemed like a good idea. But then one thing led to another until Terri was on the strike committee. They were going to demand legislation to protect precarious workers, humans and autos alike.


I’m even less good at fiction than at non-fiction, so I hope it wasn’t too trivial. I mostly wrote it as a way for me to think through the boundaries between thinking and executing an algorithm. Somehow some ethical considerations managed to sneak in. There are many places where I wasn’t clear or where I am interested in issues that are too complex for a short story, so here is the seloC Notes version (where I explain at even more length than the original). They are also my own working notes towards understanding these topics. So far, more questions than answers, but that is what I think philosophy is all about.

What is it like, to want to do something? How is it different from having a programmed or otherwise inbuilt goal? It can be a conscious goal, but it need not be. You can find yourself looking in the fridge selecting sandwich ingredients without even realising it, if you were concentrating on something else. How would Terri ‘see’ her destination. Not like a regular Uber or cab driver, by looking at words on a screen. It comes directly through one of her sensory channels. How does your internal map work as you navigate to meet someone? It’s not overlaid, augmented-reality style, on your image of the world, you can reliably find your way most of the time while your attention is on other things, unless you’re in a strange place. What is it like for a London cab driver who has trained for years to be familiar with The Knowledge, as they call it, of every little side street.

Can autos be slaves? Horses are more capable than they are likely to be for at least a few decades, but many of those have owners and we don’t call them slaves. If horses could talk, would that make a difference? For that matter, many people are still slaves, but we don’t do much about it, so it’s not likely that we will extend our objections to slavery to cover autos as well unless we were to change some of our moral categories. How could that happen?

Even without the moral concerns that, in law at least, freed human slaves, there was the economic consideration for work that is not constant: it is cheaper to hire workers as needed than to maintain slaves when they were not being productive. So my allegory supposes the same route is taken by autos. They might also need a group of humans with similar “interests”, so I introduced one. If humans and machines, in spite of many differences, had similar economic interests, perhas a union could give some kind of membership to a machine that could help the cause? At some point, the “interests” of a manufactured device that had to acquire its own resources become less and less in need of scare-quotes as those interests .

Does a cockroach have “interests” or interests? Does a chimp?

Human language is a web of analogies, shaped by many forces. We make words mean what we want them to mean, and the meaning changes over time. Marketing agencies and politicians know well how to shape discourse. The terms “Artificial Intelligence” and “Smart” don’t mean exactly what they did 10 years ago. A lot of our current acceptance of what is now termed AI and smart would not have happened 10 or more years ago, but because they have a “cool” factor, it is to the advantage of the big

Internet and device companies to convince us that they are already selling that. It will soon make less sense to question whether they are “really” intelligent, because the meaning will have shifted.

How intelligent do you think Terri is? Is she conscious?

1. Dennett, 2013, “Intuition Pumps and other Tools for Thinking”, W.W. Norton

Science and public policy

The editorial in this week’s Science1 is called The science-policy interface. The editorial itself admits that this is a “well worn, long-standing question”. Why is this? The editorial provides a hint when it says that “Providing scientific advice to government takes place within an ecosystem. It is a combination of actors who are both internal and external to government.”

Although the point of the editorial is to draw attention to the International Network for Government Science Advice (INGSA) forum, it does spend some time on the wider question.

I take this as a hint to go wider still. There are many more actors within this “ecosystem2” than are considered in the editorial. The actors in the editorial seem to be those who are providing fact-based advice, at least nominally accepting that evidence and rational argument are required, even if that is in the form of opinion polls and anecdotes. However, we need to acknowledge that there are others who are less inclined to play by the restrained rules implied by the editorial. These are the lobbyists and other powerful actors whose influence is based on their money and connections. These are also likely to use the techniques of rhetoric and half-truths and lies used by those who want to persuade people to points of view to the actors’ advantage.

This invites the next question: should those of us on the side of evidence and rationality stick to those tools of our trade, or should we go beyond that?

Figure 1 shows some of the primary influences on policy. The social climate influences and is influenced by most of the actors but is probably most strongly influenced by the actors in the middle column, including the government itself, and it most strongly influences the electorate.

Policy influences
Policy influences

How much should scientists use tactics such as lobbying and advertising techniques to persuade the government and electorate to adopt policies based on evidence and logic? How much should they use them to persuade the government where the policy is directly related to the science, such as public funding for science, especially where evidence is hard to come by?

There is significant risk (and ethical concerns) that using those techniques will reduce public trust in scientists. However, that leaves us with the challenge of countering the massive lobbying and media campaigns to deny science and reduce its role in decision making. What should our counter be, to the climate change denial funded by enormously wealthy fossil fuels companies and by tobacco companies? The poll numbers suggest that they have had excessive influence, detrimental to the public interest, on these and other topics.

The tobacco industry is doing less well these days, thanks to public campaigns against their products and press engagement that has publicized at least some of the tactics they used to sell their products. Law suits for damages to health have helped.

That suggests that similar tactics may work against the fossil fuel giants and more generally against other lobbyists and advertisers. Lawsuits may be more difficult in the case of climate change as it is more difficult to demonstrate causality for specific damages and more difficult to identify those responsible (is the producer liable, or the consumer).

However, I suggest that in the long run, more benefit will come from efforts to change the social climate. This can have a longer term effect (although with some chance of rapid changes sweeping through). Although education is a big part of this, requiring changes to funding, curriculum and techniques, it also involves professional and social media generating sufficient interest in Science, Technology, Engineering and Mathematics (STEM) that means that the education is sustained for a lifetime and that people will seek out scientific content as much as possible, as well as learning and using the thinking tools that will allow them to combat the misinformation that is everywhere.

What does this mean in practice? It means having more scientists and their allies involved in science communication. Not only communicating the cool facts, but also changing the image of what science is all about and why non-scientists should be more interested, as well as giving more insight into what scientists are like.

1. Science, 2 September 2016 doi: 10.1126/science.aai8837

2. The use of “ecosystem” in a metaphorical sense is not to be taken too seriously, but it does invite some analogies. Where are the predators and parasites? It’s not clear that any actors literally eat others, but they do compete for resources, and analogies to parasites within actors can be imagined.