Such issues help to clarify what constitutes asustainable society. The fact that problem-solving systems seemto evolve to greater complexity, higher costs, and diminishingreturns has significant implications for sustainability. In time,systems that develop in this way are either cut off from furtherfinances, fail to solve problems, collapse, or come to requirelarge energy subsidies. This has been the pattern historically insuch cases as the Roman Empire, the Lowland Classic Maya, ChacoanSociety of the American Southwest, warfare in Medieval andRenaissance Europe, and some aspects of contemporary problemsolving (that is, in every case that I have investigated indetail) (Tainter 1988, 1992, 1994b, 1995a). These historicalpatterns suggest that one of the characteristics of a sustainablesociety will be that it has a sustainable system of problemsolving-one with increasing or stable returns, or diminishingreturns that can be financed with energy subsidies of assuredsupply, cost, and quality.
It is not that research, education, regulation, andnew technologies cannot potentially alleviate our problems. Withenough investment perhaps they can. The difficulty is that theseinvestments will be costly, and may require an increasing shareof each nation's gross domestic product. With diminishingreturns to problem solving, addressing environmental issues inour conventional way means that more resources will have to beallocated to science, engineering, and government. In the absenceof high economic growth this would require at least a temporarydecline in the standard of living, as people would havecomparatively less to spend on food, housing, clothing, medicalcare, transportation, and entertainment.
Perfect analysis on the problem with problem solving. In maths this takes the form of rich tasks, extended problems, multi component questions, investigations and questions requiring just the schema described in the article. I’ve just taken a lower ability year 9 class with appalling prior behaviour on a systematic stepped algebra course, culminating on a chapter with problems in other contexts that require algebraic solutions. Each sub component of solving equations drilled in separate lessons, leading to procedural fluency in solving linear equations. Curiously, the chapter involving worded questions and diagrams went extremely badly on the first attempt. They needed explicit worked examples similar to the questions they were faced with. So, with clear didactic modelling on the board, they gained confidence and became increasingly independent in solving the problem solving questions. They had all the schema required to ‘do the math’ but couldn’t connect this to the problems at hand. Take away message; even problem solving needs explicit instruction, and filling a pupils mind with (domain specific) schema to draw upon supports better (domain specific) problem solving.
The icing on the cake, the chapter on problem solving using algebra was sourced from a traditional GCSE (intermediate level) text book. This ONLY worked because of the traditional drilling of component skills as in good old fashioned math teaching. Even more impressive for me considering this is an inner city difficult year 9 class who are pretty impossible without me there, and because of their prevailing attitude to learning have learnt very little in year 7&8.
This is exactly what happens in lessons. If students spend lesson time trying to solve problems – whether that’s writing an essay, choreographing a gymnastic routine, or using a computer program – they’ll be applying the only means a novice has available: means-end analysis. Even if they successfully solve the problem, the likelihood is they won’t remember the solution. But, if students spend lesson time having the patterns of problems explicitly modelled and then practise retrieving solutions, they’re likely to build up the schema necessary to solve future problems.
In , John Sweller explains why problem solving is an inefficient way to build up the schemas required for expert performance. The attention and processing required to engage in means-end analysis results in less capacity to store solutions in long-term memory in a way we can be easily accessed in the future. In order to build up useful schema, Sweller says “a problem solver must learn to recognize a problem state as belonging to a particular category of problem states that require particular moves.” This takes attention which, if it is being used to search for solutions, will not be available to recognise patterns.
A recurring constraint faced by previous societieshas been complexity in problem solving. It is a constraint thatis usually unrecognized in contemporary economic analyses. Forthe past 12,000 years human societies have seemed almostinexorably to grow more complex. For the most part this has beensuccessful: complexity confers advantages, and one of the reasonsfor our success as a species has been our ability to'Increase rapidly the complexity of our behavior (Tainter1992, 1995b). Yet complexity can also be detrimental tosustainability. Since our approach to resolving our problems hasbeen to develop the most complex society and economy of humanhistory, it is important to understand how previous societiesfared when they pursued analogous strategies. In this chapter Iwill discuss the factors that caused previous societies tocollapse, the economics of complexity in problem solving, andsome implications of historical patterns for our efforts atproblem solving today. This discussion indicates that part of ourresponse to global change must be to understand the long-termevolution of problem-solving systems.
1. A problem is an opportunity for improvement.A problem canbe a real break, the stroke of luck, opportunity knocking, a chance toget out of the rut of the everyday and make yourself or some situationbetter. Note that problems need not arrive as a result of externalfactorsor bad events. Any new awareness you have that allows you to seepossibilitiesfor improvement brings a "problem" for you to solve. This is why themostcreative people are "problem seekers" rather than "problem avoiders."
2. A problem is the difference between your currentstate and yourgoal state. A problem can result from new knowledge orthinking. Whenyou know where you are and where you want to be, you have a problem tosolve in getting to your destination. The solution can and should befunand exciting as you think over the various possible solution paths youmight choose. When you can identify the difference between what youhaveand what you want, you have defined your problem and can aim towardyourgoal.
In this era of global environmental change we facewhat may be humanity's greatest crisis. The cluster oftransformations labeled global change dwarfs all previousexperiences in its speed. in the geographical scale of itsconsequences, and in the numbers of people who will be affected(Norgaard 1994). Yet many times past human populations facedextraordinary challenges, and the difference between theirproblems and ours is only one of degree. One might expect that ina rational, problem-solving society, we would eagerly seek tounderstand historical experiences. In actuality, our approachesto education and our impatience for innovation have made usaverse to historical knowledge (Tainter 1995a). In ignorance,policy makers tend to look for the causes of events only in therecent past (Watt 1992). As a result, while we have a greateropportunity than the people of any previous era to understand thelong-term reasons for our problems, that opportunity is largelyignored. Not only do we not know where we are in history, most ofour citizens and policy makers are not aware that we oughtto.
This argument, developed and tested to explain whysocieties collapse (Tainter 1988), is also an account ofhistorical trends in the economics of problem solving. Thehistory of cultural complexity is the history of human problemsolving. In many sectors of investment, such as resourceproduction, technology, competition, political organization, andresearch, complexity is increased by a continual need to solveproblems. As easier solutions are exhausted, problem solvingmoves inexorably to greater complexity, higher costs, anddiminishing returns. This need not lead to collapse, but it isimportant to understand the conditions under which it might. Toillustrate these conditions it is useful to review three examplesof increasing complexity and costliness in problem solving: thecollapse of the Roman Empire, the development of industrialism,and trends in contemporary science.
One outcome of diminishing returns to complexity isillustrated by the collapse of the Western Roman Empire. As asolar-energy based society which taxed heavily, the empire hadlittle fiscal reserve. When confronted with military crises,Roman Emperors often had to respond by debasing the silvercurrency (Figure 4.2) and trying to raise new funds. In the thirdcentury A.D. constant crises forced the emperors to double thesize of the army and increase both the size and complexity of thegovernment. To pay for this, masses of worthless coins wereproduced, supplies were commandeered from peasants, and the levelof taxation was made even more oppressive (up to two-thirds ofthe net yield after payment of rent). Inflation devastated theeconomy. Lands and population were surveyed across the empire andassessed for taxes. Communities were held corporately liable forany unpaid amounts. While peasants went hungry or sold theirchildren into slavery, massive fortifications were built, thesize of the bureaucracy doubled, provincial administration wasmade more complex, large subsidies in gold were paid to Germanictribes, and new imperial cities and courts were established. Withrising taxes, marginal lands were abandoned and populationdeclined. Peasants could no longer support large families. Toavoid oppressive civic obligations, the wealthy fled from citiesto establish self-sufficient rural estates. Ultimately, to escapetaxation, peasants voluntarily entered into feudal relationshipswith these land holders. A few wealthy families came to own muchof the land in the western empire, and were able to defy theimperial government. The empire came to sustain itself byconsuming its capital resources; producing lands and peasantpopulation (Jones 1964, 1974; Wickham 1984; Tainter 1988, 1994b).The Roman Empire provides history's best-documented exampleof how increasing complexity to resolve problems leads to highercosts, diminishing returns, alienation of a support population,economic weakness, and collapse. In the end it could no longerafford to solve the problems of its own existence.