Do Humans Satisfice or Optimize?

6.5.1 Herbert Simon

It is within the context of Administrative Behavior that Herbert Simon first defined the word satisficing as both ‘[looking] for a course of action that is satisfactory or “good enough”’ and making ‘choices without first examining all possible behaviour

alternatives and without ascertaining that these are in fact all the alternatives’ (1965, xxv-xxvi). It is a term that defines a proposed behavioural strategy that results from our human nature as boundedly rational beings (Simon 1945; Simon 1957; Simon 1976; Simon 1997). The logic behind this argument is that it is a monumental, time-consuming, and potentially impossible task to consider every single possible option or even realize what every single possible option and outcome is of what may be infinite options and resulting outcomes when making decisions. An example is given of a British politician deciding whether or not to support legislation on marriage tax

bonuses. Would they think of the impact their decision could have on the clover crops and bee population (Simon 1957, 82)? Apparently in England there is a strong

correlation between older, single women and the size of clover crops due to the habit (at least in the mid-20^th century) of older, single British women keeping cats and the cats eating mice, and this impacting the bee population, leading to a significant change in the size of clover crops in areas with many single, older British women (Simon 1957, 82). Of course, this assumes a connection between marriage tax bonuses and the number of single women, which could very well not be there.

The result of this inability to consider all possible options and resulting outcomes when making a decision is that the human mind practices satisficing – choosing the good enough option (Simon 1957; Simon 1976; Simon 1997).

In Administrative Behavior, humans are only boundedly rational and as boundedly rational beings we can only satisfice (Simon 1945; Simon 1957; Simon 1976; Simon 1997):

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‘In one sentence, the thesis of Chapters IV and V is this: The central concern of administrative theory is with the boundary between the rational and the non-rational aspects of human social behaviour. Administrative theory is peculiarly the theory of intended and bounded rationality – of the behaviour of human beings who satisfice because they have not the wits to maximize’ (Simon 1957, 118).

The full theory of bounded rationality, out of which the term satisficing originates, takes a full book to describe and cannot be over simplified as simply ‘good enough’

behaviour. For example, Simon (1957, 41) considered acting in one’s own interest over the interest of the organization to be non-rational behaviour – a point that could definitely be contested!

Intuitively, it may be tempting to automatically accept and apply this theory on human nature to route studies (for example, see Branting 2012). Certainly, Simon’s ideas remain an important part of business studies as reprinting of the latest edition of Administrative Behavior continues. Nonetheless, a group of politicians making a legislative decision on marriage bonuses is not entirely analogous to the gradual formation of hollow ways through the repeated decision by people to travel a single path across the landscape, despite their increasing knowledge of the landscape both from personal experience and shared experience. People have the potential to learn from experience and, over time, to optimize.

6.5.2 Human Experiments

Evidence from experiments within the field of route studies suggest that over time people optimize their travel as knowledge of their landscape increases (Kneidl and Borrmann 2011). Kneidl and Borrman (2011, 3) were able to distinguish between three types of pedestrian in their study on human wayfinding and navigation:

• ‘Pedestrians who are familiar with the location and know the best way to their destination

• Pedestrians who are not familiar with the location, but try to keep as close as possible to the airline [beeline/straight line] to their destination

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• Pedestrians who are not familiar with the location and make their decisions based on local criteria.’

The study asked students to travel to a well-known (and undisclosed) landmark from one of four corners of the Munich Technical University campus in groups of two to four without a map. The subjects were also instructed to record each street along their journey (Kneidl and Borrmann 2011, 3). Upon returning to campus, the students drew their route on a map, filled in a survey that inquired about their familiarity with the city centre and whether or not they felt they had taken the fastest route (Kneidl and Borrmann 2011, 3, 5). The majority (89.54 percent) thought they had, but only about half (51.44 percent) indicated they were familiar, quite familiar, or very familiar with the area. Unfortunately, the success rates are not published. Rather the routes are plotted together on a single map, but it is clear that about half took the fastest optimal route indicated by a computer algorithm (Kneidl and Borrmann 2011 figs. 4 and 5). What is most fascinating about this experiment and its results is that, if the methodology described is complete, the researchers assumed that the subjects would (or would try to) take the fastest route and 89.54% of the subjects believed they had succeeded in doing so, despite no specific instruction to take the fastest route (or any other optimal route).

In a very different experiment run by psychologists, 20 subjects from Lancaster, UK were asked to solve a type of travelling salesperson problem: create a circuit tour of 10 locations, not visiting a single location more than once (Chronicle, MacGregor, and Ormerod 2006). In a follow on study by the same researchers, 112 subjects from the Introduction to Psychology course at the University of Hawaii at Manoa were asked to solve the same problem with 15 location points. In both cases, subjects were asked to generate optimal shortest routes and pessimizing longest routes. Among the initial 20 subjects, each of whom solved five variations of the problem, 31 of the 100 shortest tours were optimal, but none of the subjects managed to pessimize the longest possible solution to the problem (Chronicle, MacGregor, and Ormerod 2006, 77). In the follow on study, the subjects were also significantly better at generating the shortest solutions than the longest solutions (Chronicle, MacGregor, and Ormerod 2006, 79–80).

165 6.5.3 Archaeology

Archaeological evidence for (or against) optimal behaviour in wayfinding and navigation has, until recently (de Gruchy 2016), been limited by an inability to

quantitatively assess an optimal route model against a preserved route. However, the debate on whether people optimize or not has a much longer history in the

subsistence patterns of archaeological hunter-gatherers, where optimal (energy) behaviour has traditionally been assumed in optimal foraging theory. With its origins in biology (MacArthur and Pianka 1966), the theory was borrowed by archaeology quickly and used to make interpretations about subsistence strategies and settlement patterns of anthropological and archaeological hunter-gatherer populations (Lee 1969; Yellen and Harpending 1972; Bayham 1979; see also Smith 1979). The theory encompasses several models including: diet breadth, patch choice, central place foraging models, and margin value theorem (Kelly 2013, 46–70), and there is no reason why other models based on additional evidence could not be constructed within the framework of optimal foraging theory.

Like optimal route models, optimal foraging models assume that the subject people are knowledgeable about their landscape and able to make informed decisions.

Interestingly, there may be a practice analogous to topographical gossip for optimal foraging in which ‘men and women note the presence of plants, animal tracks, spoor, water sources burrows, and nests and later share this information with other’ (Kelly 2013, 63). So, while humans may not have ‘perfect information about their

environment’ (Kelly 2013, 70) shared experience ensures ‘what should be obvious:

foragers know what is going on in their environment’ (Kelly 2013, 63). It is no surprise, then, that optimal foraging theory has been found to successfully model the actual behaviour of hunter-gatherers in ethnographic examples and to fit well with archaeological data (Broughton 1997; Stiner, Munro, and Surovell 2000; see also Smith et al. 1983; Zeder 2012; Kelly 2013, 40–76).

Optimal foraging theory is not perfect and, for some, it is believed that in some cases an alternative paradigm, niche construction theory, can provide a better approach (Smith 2014, Zeder 2012). Niche construction theory differs from optimal foraging theory because it is based on the recognition that people ‘modify their environment

166 to increase the relative abundance and predictability of plant and animal researches within their research catchment areas’ (Zeder 2012, 257). Others think a fusion of optimal foraging theory and niche construction theory could provide a useful framework, at least for the question of the origin of agriculture (Gremillion, Barton, and Piperno 2014a; Gremillion, Barton, and Piperno 2014b). The fusion proposed by Gremillion et al. (2014a) is possible, because niche construction theory does not inherently contradict optimal behaviour. Nonetheless, it should be noted that the issue of whether or not people tend to behave optimally is debated in the field of foraging strategies (Zeder 2012, 255–56).

Overall, optimal foraging models are really quite analogous to optimal route models in that ‘Foraging models…claim to model reality at some level of specificity if hunter-gatherers [people] are behaving according to a model’s set of goals and conditions.

Optimization models are heuristics, they do not provide a priori answers and

explanations. By predicting which resources a forager will take if resource are ranked only in terms of their search costs and post encounter return rates, for example, the data collected to test optimal-foraging models can flag those resources that are taken or ignored for reasons other than energetics’ (Kelly 2013, 76). Likewise, optimal route models based on physical variables can highlight routes or route segments where cultural variables are responsible for directing the nature of travel. Used this way, optimal foraging models do not expect people to behave optimally, but it is assumed that they have the ability to do so. The same sentiment was expressed already in the early 1980s by Eric Alden Smith et al. (1983, 626):

‘optimization assumptions should be viewed as potentially useful starting points for building models rather than as Panglossian conclusions about the operations of the real world…Like any optimization analysis, an optimal foraging model must specify a currency (such as energy), a goal (such as maximizing foraging efficiency), a set of constraints (factors that limit the range of options…), and a set of options (choices left open to the actor).’

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