Blink, the new book by the author of Tipping point. Intuitive decision making.
Thursday, March 31st, 2005For those who are familiar with mayer-briggs, and Jung Typology.
The word intuition should be a description of type of brain function. The ability to consider multiple variable and come up with a summarizing conclusion rather than focus on individual aspects and the finite details of each variable.
Some say it is more instinct than pre-thought and quick brain process, others believe that it is the work of the 18K synopsis firing in our brains other than the 2K synopsis of forebrain/ front cerebral firing that is usually considered conscious thought. Inversely, unconscious thinking process that come up with conclusion and injects into the forebrain or human consciousness is considered intution.
Blink is a look into how this may work, and the best way to make quicker and accurate seemingly instinctive decisions.
I have yet to read the book, however I can see how Hicks law would play into this.
Hick’s law states that response times (RTs) increase in proportion to the logarithm of the number of potential stimulus-response (S-R) alternatives. We hypothesized that time-consuming processes associated with response selection contribute significantly to this effect. We also hypothesized that the latency of saccades might not conform to Hick’s law since visually guided saccades can be automatically selected using topographically organized pathways that convert spatially coded visual activity into spatially coded motor commands. We evaluated these hypotheses by examining three response modalities for their compliance with Hick’s law: saccades directed to a visual target (prosaccades), saccades directed away from the target (antisaccades) and manual responses in which each digit was associated with a specific target location (key-press responses). Both antisaccades and key-press responses conformed to Hick’s law but saccade latencies were completely unaffected by S-R uncertainty. The significance of these findings is considered in terms of the processes of response selection and premotor programming. Saccades is: The rapid eye movement occurring during reading and when viewing visual patterns, and considered to betray the thought process, and show up as a "tell" in poker. This is why Poker players will use sun glasses during game player.
The interesting this conncected to the last posting is that reaction speed slows drastically, when Sympathetic Nervous response is triggered, and the % of success of choosing the right response is down to only a 57% chance. The optimal heart rate for dealing with decisions is 115 to 140. From 140 to 220, the bosy basically starts shutting down and breaking down. (This is referred to as Choking)
A good human interface, or "user friendly" is one which lowers the Reaction Time, to anything by making the choices and process seem intuitive.
As " conncectors" referred in Tipping point close the gap of 6th degree of separation to be able to reach anyone on the planet, quick decision references, trained responses, precepts, and minimizing the choices is the way to lower reaction time, and utilize intuition to maximize Hick’s law.
If looking at a decision matrix and decision tree, limiting the acceptable factors, and creating criterias (sweeping assumptions) would lower the number of choices and therefore make it easier to compute a logic decision. If done, with the sub-conscious, then the thought process is considered "intuitive".
THE DECISION MATRIX
RELATING THEORY TO PRACTICE
The "decision matrix" is a useful tool to help make choices between complex alternatives. This particular heuristic can serve a different purpose at each stage in our work. First, it can help to an analyze the problem, the task, or the objective by having them broken down into a number of requirements. Once the requirements have been determined, the heuristic helps sorts them in their relative importance or weights.
FIGURE 1 Decision Matrix
The same heuristic can be used as a "sub-matrix" for combining the various benefits that constitute a complex requirement and for noting their relative contribution towards meeting this requirement. Finally, the heuristic provides a framework for evaluating various courses of action, or choices, in order to select:
- The best combination of desirable characteristics for a given level of resources, or
- A given combination of desirable characteristics for the least cost in resources.
More important, enumerating and putting the requirements into this framework makes people think about the requirements and the alternatives at a detail level. The matrix, of course, does not make decisions, it simply lines up the information for the exercise of judgment by the person responsible.
It serves as a two-dimensional checklist which forces one to consider, one at a time, the major factors leading to a decision. For instance, they must choose between the best combination for the given resources, or the least resources for a given combination. Trying for both the best combination and the least resources is like trying to get both ends of a seesaw to go up at the same tune. No technique of can achieve this.
Where to Start
Whether studying a wafer-thin integrated circuit or a new aircraft, design must start by identifying and evaluating what are the important attributes of the objective (customer wants, the desired benefits etc.). These are listed along the top of the matrix shown in Figure 1.
The letters and their subscripts are simply addresses on the matrix. They are used for discussion to identify location. They do come in handy in understanding all sorts of arrays presented in this fashion.
In each locations labeled B1 to Bn write each of the desired attributes. Using only those which differ among the various alternatives being considered. If all the alternatives are equal with respect to an attribute, obviously it would not be meaningful evaluating the alternatives.
Weighting Technique
Under objective, at W1 to Wn, is put a number, the weighting, showing the relative importance of the attribute with respect to the others. It should be emphasized that the sum of the weightings must equal unity. The weighting cannot be add indiscriminately without adversely changing the others.
If we are very familiar with the product and the requirements, the weights may be assigned directly. Otherwise, we can start by dividing 1 equally among the requirements, and then varying the weights as the relative importance of each requirement is compared with the others, keeping the sum always equal to 1.
Normalized Utility
The e in the cells represents the "normalized utility" of the alternative with respect to the requirement heading the column. "Just how well does this alternative satisfy the requirement heading this column." If we refer to e12 (e one,two), we mean how well does alternate C1 meet the requirement B2.
Commensurable Units
We cannot compare alternatives with respect to different attributes and simply add them up. We must consider each alternative with respect to one attribute at a time in terms of a normalized utility scale.
The Standard Scale
Fully as important as finding the commensurable utilities is the standard scale. The normalized utility scale extends between two agreed upon values. These values establish upper and lower limits for the parameter and their utility.
Simply put, the limits set at the least acceptable measurable value of the parameter, and the best practical value (Fig. 4). The direct measure of performance can be related to utility by first identifying the values at which the performance, (a) begins to improve the product and, (b) beyond which it cease to improve the product. These are the performance values that correspond to the minimum and maximum values on the normalized utility scale.
Adjusting for Utility
When Daniel Bernoulli wrote in 1738 that the utility of money to a man declined in proportion to how much money he already had, and that this decline was not uniform but logarithmic, he was stating the law of marginal utility. For our purpose it simply means that the line on the graph which transforms raw data into commensurable units may not always be a straight line or a pat mathematical function.
The fact that economy of production may decrease at a different rate than the increase in price is shown by the curvature of the graph in Fig. 5. This curve not only normalizes the "dollar-cost of production" into the 70 to 90 normalized utility scale, but it also adjusts the dollar figures to take into account the "utility" of money to the customer.
It turns out, however, that the concept of utility goes beyond money. The utility of dimensional accuracy is not uniformly proportional to the actual change in tolerances (Fig. 6), and the value to the user of decreased weight is not uniformly proportional to the change in weight (Fig. 7).
The Final Choice
By multiplying the normalized utility en in each cell by the weighting factor Wn heading its column we get that the contribution that the alternatives performance with respect to that requirement makes to the overall value (utility) of that alternative. All that remains is to sum the utilities along the row and find a number expressing the relative utility of the of that alternative with respect to the others. This relative utility includes a balance consideration all the requirements.
In the example Fig. 2, it is seen that electroforming provides the highest expected value, for this particular application. And the reasoning is visible. Others can also see the reasoning that went into this numerical rating. They also see that fabrication could have been the best choice, had cost been more important than dimensional accuracy.
This example illustrates another valuable property of this type of matrix. It is not only useful in ordering our alternatives in a rational manner, but it makes our reasons for the choice visible. persuading others that the preferred alternative should be implemented becomes much easier.
Example Decision Matrix for Leaf Disposer
FIGURE 8 Alternate designs
FIGURE 9 Matrix Form
FIGURE 13 Finished decision matrix.
The order of the "SUM" gives the order of preference according to the listed criteria.
- Leaf Bailer at 79
- Vacuum Collector at 75.7
- Chemical Decomposer at 68.8
- Shredder at 65.9
The optimal decision here is 79.
However, what if one automatically tossed out anything with a too low utility or too low weight?
and decreased choices down to just 2. The calculation of each option would become much more simple, and faster, and therefore decreasing reaction time, according to Hick’s Law.
Decision Tree: (The visually represented decision method)
Decision Tree Analysis
- Choosing Between Options by Projecting Likely Outcomes
How to use tool:
Decision Trees are excellent tools for helping you to choose between several courses of action. They provide a highly effective structure within which you can lay out options and investigate the possible outcomes of choosing those options. They also help you to form a balanced picture of the risks and rewards associated with each possible course of action.
Drawing a Decision Tree
You start a Decision Tree with a decision that you need to make. Draw a small square to represent this towards the left of a large piece of paper.
From this box draw out lines towards the right for each possible solution, and write that solution along the line. Keep the lines apart as far as possible so that you can expand your thoughts.
At the end of each line, consider the results. If the result of taking that decision is uncertain, draw a small circle. If the result is another decision that you need to make, draw another square. Squares represent decisions, and circles represent uncertain outcomes. Write the decision or factor above the square or circle. If you have completed the solution at the end of the line, just leave it blank.
Starting from the new decision squares on your diagram, draw out lines representing the options that you could select. From the circles draw lines representing possible outcomes. Again make a brief note on the line saying what it means. Keep on doing this until you have drawn out as many of the possible outcomes and decisions as you can see leading on from the original decisions
Calculating Tree Values
Once you have worked out the value of the outcomes, and have assessed the probability of the outcomes of uncertainty, it is time to start calculating the values that will help you make your decision.
Start on the right hand side of the decision tree, and work back towards the left. As you complete a set of calculations on a node (decision square or uncertainty circle), all you need to do is to record the result. You can ignore all the calculations that lead to that result from then on.
Calculating The Value of Uncertain Outcome Nodes
Where you are calculating the value of uncertain outcomes (circles on the diagram), do this by multiplying the value of the outcomes by their probability. The total for that node of the tree is the total of these values.
In the example in Figure 2, the value for ‘new product, thorough development’ is:
| 0.4 (probability good outcome) x £500,000 (value) = |
£200,000
|
| 0.4 (probability moderate outcome) x £25,000 (value) = |
£10,000
|
| 0.2 (probability poor outcome) x £1,000 (value) = |
£200
|
|
+
|
£210,200
|
Figure 3 shows the calculation of uncertain outcome nodes:
Note that the values calculated for each node are shown in the boxes.
Calculating The Value of Decision Nodes
When you are evaluating a decision node, write down the cost of each option along each decision line. Then subtract the cost from the outcome value that you have already calculated. This will give you a value that represents the benefit of that decision.
Note that amounts already spent do not count for this analysis - these are ’sunk costs’ and (despite emotional counter-arguments) should not be factored into the decision.
When you have calculated these decision benefits, choose the option that has the largest benefit, and take that as the decision made. This is the value of that decision node.
Figure 4 shows this calculation of decision nodes in our example:
In this example, the benefit we previously calculated for ‘new product, thorough development’ was £210,000. We estimate the future cost of this approach as £75,000. This gives a net benefit of £135,000.
The net benefit of ‘new product, rapid development’ was £15,700. On this branch we therefore choose the most valuable option, ‘new product, thorough development’, and allocate this value to the decision node.
Result
By applying this technique we can see that the best option is to develop a new product. It is worth much more to us to take our time and get the product right, than to rush the product to market. It is better just to improve our existing products than to botch a new product, even though it costs us less.
Key points:
Decision trees provide an effective method of Decision Making because they:
- Clearly lay out the problem so that all options can be challenged
- Allow us to analyze fully the possible consequences of a decision
- Provide a framework to quantify the values of outcomes and the probabilities of achieving them
- Help us to make the best decisions on the basis of existing information and best guesses.
As with all Decision Making methods, decision tree analysis should be used in conjunction with common sense - decision trees are just one important part of the decision making process.
One can see multi-areas where quick elimination of choices, and pre-assumptions, choosing limits would decrease choices. Hick’s Law says that the less number of large options, the faster the decision can be made.
The rule of 4 says, that any choice with more than 4 variable would become hard to calculate for a human. This means choosing less than 2 weights, and 2 utilies, would make for much faster calculation.
The difference is simple.
calculating the 124323.50925803 x 98791.294924 becomes much simplier if it is simplified to 124000 x 98800. It become even more simple if we said that any factor that has a variable less than 100000 just won’t be considered. (For instance, anything with a less than 60% chance of success will not be considered), decreases the number of total calculations that needs to be made.
Quick assumptions and quick pre-calculated answers (conclusion from experience) will further toss out options, or act as a calculator or excell sheet to the decision making process, therefore making the decision easier to reach.
Therefore the basis for the book Blink. (I believe)
Tony









