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Indigenous Knowledge Systems, The Cognitive
Revolution, and Agricultural Decision Making
Christina H. Gladwin
Christina H. Gladwin is Associate Professor in the Food and Resource Economics Department at the University of
Florida, Gainesville. Her research interests include the cognitive relationship between norms, plans, and decision
processes and large-scale shifts in norms and choice. The research for this paper was initiated while she was a
Rockefeller post-doe assigned to the
International
Fertilizer Development Center.
ABSTRACT: Increasingly, it is acceptedwisdomfor agricultural scientists to getfeedback from indigenous peoples--peas-
ants--about new improved seeds and biotechnologies before their official releasefrom the experiment station. What is not
yet accepted udsdom is the importance of cognitive science to research onfarmer decision making, especially of the type
"Why don't they adopt." In this paper, the impact of the cognitive revolution on models offarmer decision making is
described, and decision making models before and after the cognitive revolution are contrasted. An example of a decision
model after the cognitive revolution is given by the Malawifarmer's decision whether to use chemical fertilizers or organic
fertilizers or both. Results of testing the model show that in Malawi, smallholdsrs' lack of capital and credit are more
important factors constraining use of chemical fertilizers than are indigenous beliefs in organic fertilizers or fears of a
future dependency on chemicals.
The schoolofthoughtnowdubbed IKS, indigen-
ous knowledge systems, aims to elicit the expert
systems ofindigenouspeoples--peasants---whoare
sometimes not thought ofas experts. These knowl-
edge systems are brought back to agricultural re-
search centers and ministries of agriculture and
used to educate agricultural scientists and policy
makers so that they can design better technologies
and policies to improve peasants' standards of liv-
ing. The idea that local knowledge is a valuable
resource to be understood and used to alleviate
poverty is beginning to diffuse throughout the
Third World in practical applications to develop-
ment problems, and in the academic literature, as
is evidencedby the papers in this volumeand others
(Brokensha, 1989). Starting with the collection of
the same name, IKS scholars have described the
expert systems of peasants to do any number of
things: to farm, to fertilize, to manage their soils
and natural resources in a sustainable way, to adopt
or not adopt new '~improved"technologies, to take
risks, to make a living (Brokensha, Warren and
Werner, 1980). Farming systems programs in in-
ternational agricultural research centers have seen
the value ofknowing'~ow the farmers think" about
their crops, pests, forests, and water resources
(CIMMYT, 1984; Matlon, Cantrell, King, and Be-
noit-Cattin, 1984), before agricultural scientistsde-
sign improvedtechnologiesfor them. Increasingly,
it is accepted wisdomfor an agronomistto get feed-
back from peasants about new improved seeds and
biotechnologies before their official release from
the experiment station.
It is also generally recognized that the goals of
the IKS school go way back to the anthropologist
Malinowski (1922) who aimed to "grasp the native's
point ofview, his relation to life, to realize his vision
of his world." To see the insiders' world through
the insiders' eyes has long been the aim of enthog-
raphers, whose work describes a culture from the
"native's" or insider's point of view and not from
the researcher's or outsider's point ofview (Sprad-
32
Gladwin: Indigenous Knowledge Systems
ley, 1979). To minimize the researcher's own
ethnocentricity, i.e., the viewingofanother culture
through the lens of one's own cultural values and
assumptions, the enthnographer seeks to learn
from people, to be taught by them like a child is
taught. The aim is to discover the cultural meaning
of the insiders' relationships, native terms, rules,
and way of life (Spradley, 1979: 3). This is a far
different goal from that of collecting data about
people and testing a model based on the outsider's
view. Models of indigenous knowledge systems
should thus contain "emic" categories, i.e., units of
meaning drawn from the culture bearers them-
selves, which can be contrasted with "etic" catego-
ries which may have meaning for researchers but
need not have meaningfor the people ofthe specific
culture under study (Pike, 1954; Harris, 1979: 32-
45).
computer; its short-term memory is limited to
roughly 5 to 7 items at a time. Indeed, people seem
to categorize or discretize variables, rather than
deal with continuous quantitative variables as a
real computer does. People use logic rather than
perform complicated mathematical operations as a
real computer does. The limited "rehersal buffer"
of humans affects the way people organize their
language and thought processes, such that they
are
"nierarchially" organized (Miller, Galanter, and
Pribram, 1960).
This revolutionary idea was taken up by the
linguist Noam Chomsky, whose '"crees" or transfor-
mational grammars spread to most knownlanguages
around the world. In the field of artifical intelli-
gence, it stimulated scientists to invent computer
programs called "expert systems" which mirrored
the way humans think, rather than expect humans
to think like sophisticated computers. In psychol-
ogy, the revolutionstimulated newtheories ofprob-
lem solving (Neweli and Simon, 1972) and plans or
scripts (Schank and Abelson, 1977), and caused the
abandonment of decision making models of "ex-
pected utility" in favor of theories of "elimination-
by-aspects" and "preferencetrees" (Tversky, 1972;
Kahneman and Tversky, 1972, 1982; Tversky and
Kahneman, 1981).
In anthropology, in resulted in the spread of
cognitivemodels oftaxonomies, schematas, and de-
cision processes, which replaced the use of
psychological traits and raw-shock tests to explain
cultural differences. Given the aims of ethnograp-
hers to modelthe insiders' point ofview and knowl-
edge, it was not surprising that the cognitive rev-
olution found the field of anthropology a fertile
ground to grow in. The products of cognitive an-
thropology can now be seen in frame analysis
(Frake, 1964), taxonomic analysis (Berlinand Kay,
1969), componential analysis (Romney and D'An-
drade, 1964), schematas or folkmodels(D'Andrade,
1981; 1987; H. Gladwin, 1974; Quinn 1990), plans
or scripts (Werner and Schoepfle, 1987), and deci-
sion tree models or tables. Before these models
are
described, a look at what decision models were like
before the cognitive revolution is in order.
What is not yet accepted wisdom is the impor-
tance of cognitive science to this school of thought
and agricultural development policies, and the in-
fluence of '%he cognitive revolution," which, since
the 1950s and 1960s, has shaken the foundations of
six social sciences: artificial intelligence, anthro-
pology, linguistics, neuroscience, psychology, and
philosophy (Gardner, 1985). Perhaps agricultural
scientists who are used to empirical laboratory ex-
periments are uncomfortablein realizingthat farm-
ers' acceptance of their new biotechnologies partly
depends on the mystery ofhow the human mind--
the ultimate black box--works. Yet as more and
more agricultm~al scientists giveup the naivenotion
that their technologies will automatically diffuse,
more and moreresearch on farmer decisionmaking,
especially of the type '~¢hy don't they adopt," is
done. One lesson gleaned from the adoption litera-
ture, by now too vast to adequately cite, is that the
success of efforts to predict farmers' decision mak-
ing depends on the underlyingassumptions that
are
made about cognition, i.e., the thought process it-
self. These assumptions changed drastically with
the cognitive revolution. Before the cognitive rev-
olution, agriculturaldecision modelsweremathemat-
ically sophisticated, with risk-aversion often being
measured by the sign of the second differential
(Schoemaker, 1982); after the cognitive revolution,
simpler decision rules and trees replaced optimiza-
tion ofacontinuous, twice-differentiablefunction.
Agricultural Decision Making Before
the Cognitive Revolution
In the field ofagricultural decisionmaking, long
dominated by (agricultural) economists, decision
models were quantitative, linear-additive, and
often normative (e.g., linear programmingmodels,
expected value and expected utility models, sto-
chastic dominance) but typically not empirically
grounded. They were not usually tested against a
set of choice data to see how well they predicted
The Cognitive Revolution
Scientific assumptions about cognition were
radically changed in 1956 with the publication of a
seemingly-innocuous article entitled, "The Magic
Number Seven, Plus or Minus Two,"by the psycho-
logist George Miller. Miller's new idea was that the
human computeris oflimited capacity, unlike a real
33
AGRICULTURE AND HUMAN VALUES--SUMMER 1989
the choices ofindividuals in a group (see Anderson,
1979; Anderson, Dillon, and Hardaker, 1977). In-
stead, they were either used as behavioral assump-
tions in a model of aggregate supply or demand, or
as normative models to tell people how they should
make decisions (Raiffa, 1968), or tested to see if
they "fit" the observed behavior of one '~represen-
tative" individual (Benito, 1976).
They were not empiricallytested against choice
data because the test was usually so complicated
that it was not worth the effort. For example, in a
test of the expected utility model, the researcher
had to measure each individual's "utility function,"
which could vary in shape across individuals de-
pending on how risk-averse they were; and then
independently measure each individual's "subjec-
tive" probability distribution which differed from
the objective probability distribution. This proved
to be such a job that it was rarely done; but text-
books described how it could be done (Anderson,
Dillon, and Hardaker, 1977). When it was done,
errors arose due to inconsistencies(Officerand Hal-
ter, 1968), or when midway through the experi-
ment, riskless gambles were replaced byriskygam-
bles, suggesting that even the same individual
might have morethan one utilityfunction(Tversky,
1967).
rules do not produce an ordinal utility function, as
micro-economictheory assumes.
Cognitivists even objectedto the linear-additive
decision models called probit analysis and logit
analysis, which have the advantage over expected
utility and linearprogrammingmodelsofbeingtest-
able with data on choicesmade by manyrather than
one individual. Unfortunately, they also are
not
cognitively-realistic models of the choice process.
No-one assigns weights to several variables and
then adds them up to determine which of several
outcomesis better; people comparealternatives one
dimension at a time. But probit and logit analyses
do have the advantage of providing a statistical
test; they thus can be used alongside rule-based
decision models to show whether there is a signifi-
cant correlation between a particular independent
variable (or decision criterion in the nile) and the
decision outcome chosen. In this way, they can pro-
vide an indirect test of a rule-based model
(Mukhopadhyay, 1984). Unfortunately, they can
provide this test for only a few of the variables or
criteria in an individual's decision process.
Cognitivists also rejected the quantitative na-
ture ofdecisionvariables in a probit or Iogit analysis
or linear programming model. Following Miller
(1956), they claimed that decision makers use dis-
crete decisioncriteriainreal-lifechoices, evenwhen
faced with a variable amenable to quantification
such as cost. The decision criteria used in decision
tree models are thus not continuous quantitative
variables; they are discrete constraints that must
be passed or satisfied (e.g., Is cost ofcar < $4000?)
or orders and semi-orders ofalternatives on aspects
(e.g., Is cost of carl < cost of car2?). An alternative
is assumed to be a set of aspects or constraints
(Lancaster, 1966; Tversky, 1972; Gladwin, 1980),
but criteriaare discrete. An algebraic form ofchoice
model results. The decision process is also assumed
to be deterministic rather than probabilistic: an al-
ternative either passes the criteria or constraints
with a probability of one or it does not. There are
thus no probabilities otherthan 1 or 0--facing the
individual on each branch, as in Raiffa (1968). A
decision tree is thus a sequence ofdiscrete decision
criteria, all ofwhich have to be passed along a path
to a particular outcome or choice.
Agricultural Decision Making After
the Cognitive Revolution
Cognitive psychologists and anthropologists in
the 1970s rejected the expected utility theory of
choice, and searched for more cognitively-realistic
models of the choice process. They claimed that
people in real-life choice contexts don't make holis-
tic assignments of utility or satisfaction to each al-
ternative inthe choiceset, and separatelyformulate
subjective probabilities (Quinn, 197~, and then pick
the alternative with the most "expected utility"
(Kahneman and Tversky, 1972, 1982). In line with
Miller's results on the limitations of human compu-
tational capacities, theyfeltthat decisionsare made
in a decomposed fashion using relative compari-
sons, because it is cognitively easier to compare
alternatives on a piece-meal basis, i.e., one dimen-
sion at a time (Schoemaker, 1982). Indeed, people
do not rank order alternatives holistically when
they make a decision. They just chose one out of
several alternatives without ranking them (Quinn,
1971), in which case the decision model is what
Arrow (1951) calls a "choice function not built up
from orderings," i.e., simply a set ofrules. In some
choice contexts, these rules may result in an incom-
plete order (Gladwin, 1975) and intransitive perfer-
ence structure (Tversky, 1969). In these cases, the
Decision Tree Modeling
Ethnographic decision tree modeling starts
fromthe assumption that the decisionmakers them-
selves are the experts on how they make the deci-
sions they make. It uses ethnographic fieldwork
techniques to elicit from the decision makers them-
selves their decision criteria, which are then corn-
34
Gladwin: Indigenous Knowledge Systems
bined in the formofa decisiontree, table, flowchart,
or set of if-then rules or "expert systems" which
can be programmed on the computer (Gladwin,
1989). There are thus two distinctivefeatures about
the method: its reliance on ethnographic fieldwork
techniques to elicit the decision criteria, and its
insistence on a formal, testable, computer-based
model of the decision process which is hierarchical
or treelike in nature.
Ethnographic decision tree modeling is not a
black box technique like some quantitive methods
(e.g., factor analysis, multidimensional scaling,
cluster analysis). Because of its dependence on
eliciting procedures, the model is culturally tuned
by some specific group of individuals, and then
tested against choice data from other individuals
in the group. 1The form of a decision tree model is
amazingly simple, with the choice alternatives in a
set at the top of the tree, denoted by { } and the
decision criteria at the nodes or diamonds of the
tree denoted by < >, and the decision outcomes
denoted by [ ] at the ends of the paths of the tree.
The decision maker starts at the top of the tree
and, independently of other decision makers, is
asked the set ofquestionsinthe criteria at the nodes
of the tree, and based on his or her responses is
"sent down" the tree on a path to a particular out-
come.
Cognitiveanthropologistsinthe 1970s and 1980s
applied these ideas to real-life choice contexts in a
number of different cultures. These included
economic decisions made by Ghanaian fish sellers
in decidingbetween markets (H. Gladwin, 1971; C.
Gladwin, 1975; Quinn, 1978), farmers' adoption de-
cisions in Puebla, Mexico (C. Gladwin, 1976, 1977,
1979a, 1979b), California families' decisionsregard-
ing the sexual division of labor within the family
for daily routine tasks (Mukhopadhyay, 1984),
farmers' croppingdecisions (Barlett, 1977; C. Glad-
win, 1983), peasants' choice oftreatment forillness
in Pichatero, Mexico (Young, 1980, 1981), U. S. car
buyers' choice of cars (H. Gladwin and Murtaugh,
1984; Murtaugh and H. Gladwin, 1980), economic
development decisions of the Navajo tribe
(Schoepfle, Burton, and Morgan, 1984), and U. S.
farmers' decisions to cut back production and sell
land daring a farm crisis (Zabawa, 1984; C. Gladwin
and Zabawa, 1984, 1986, 1987). In each case where
the methodology of decision trees has been used,
the predictability has been as high as 85 to 95 per-
cent of the historical choice data used to test the
model. These success rates are remarkable only
because the pre-cognitive decision models of ac-
cepted wisdom (e.g., expected utility) could not
even be tested to see how well they could predict
a set of choice data.
A Real-Life Example: The Malawi
Smallholder's Decision Between
Chemical and Organic Fertilizers
How do small-scalefarmers in the Third World
decide whether or not to use chemical fertilizers?
Why do or don't they use organic fertilizers(man-
ure, compost, green manure) as substitutes for
chemicals? Which constraints to chemical fertilizer
use are more important: farmers' lack ofcapitalor
credit,ortheirindigenous beliefsinorganic fertiliz-
ers (manure/compost) as the right way to fertilize
their crops, or their fear of dependency on chemi-
cals?
This decisionisa crucialone forAfrican govern-
ments facinga '%od crisis"intheircitiesand trying
toincrease thefood surplus produced by smallfarm-
ers in the countryside, because food production is
linked to quantity of fertilizerused on most food
crops. It is even more important in a country like
Malawi in southern A~ca thatisland-locked,faces
high transport costs to the sea (due to the cutting
of the Beira railroad line in Mozambique) and im-
ports all chemical fertilizeror its feedstocks. It is
a key decision for those policy makers interested
in the potential of sustainable agriculture or low-
input agriculture(Brush, 1989).The decisionmodel
in figures la, lb, lc, and ld explains why some
farmers use both chemical and organic fertilizer,
while others use only chemical, while some use only
organic (definedas animal manure, compost, orcer-
tain kinds of green manure). It also tests whether
the main reason fornonuse ofeitherkind offertilizer
is simply farmers' lack ofcash or credit,as opposed
to an indigenous trust in localorganic fertilizersor
amore invidiousfearofdependency onchemicals.
In April and May, 1987, the model was tested
with choice data on fertilizeruse ofsmallholders in
Malawi during personal interviews; the testsample
was comprised of 40 farmers in three agricultural
districts: Lilongwe, Kasungu, and Salima.~ Al-
though farmers in this sample were on average big-
ger, more experienced farmers than is the norm
(with average landholdings of 3.02 ha.), in other
respects the sample is fairly representative of
Malawi smaUholders: 26 farmers were credit club
members, and 14 were not; 22 farmers got credit
for fertilizer in 1986/87, while 18 did not. Seventeen
farmers were women householdheads, three were
couples interviewed together, and 20 were male
household heads; the sample has a high proportion
of female household heads because Dixon (1982)
estimates that women in Malawi perform about 50
percent ofthe agricultural labor. Ofthe 40 farmers
interviewed, 33 were household heads. Questions
about the sexual divisionoflabor and incomewithin
the family revealed that groundnuts is a woman's
35
AGRICULTURE AND HUMAN VALUES--SUMMER 1989
cash crop while tobacco, cotton, and hybrid maize
are men's cash crops, and local maize and beans are
grown for the whole family's consumption.
Because the outcome chosen by the farmer is
different for different crops, this model is specific
to the local variety of maize, which constitutes 90
percent of maize production and is the staple food
crop. Also for simplicity of modeling, it is here as-
sumed that every farmer incorporates some crop
residues of maize during the "banking up" of soil
around the secondary roots aider the second weed-
ing and fertilizing. This use of crop residues is no
doubt beneficial to the soft, but is not the same
terrific stuff as animal manure or compost. Hence
organic fertilizer here means manure and/or com-
post and/or green manure, but not crop residues.
The model posits that farmers must first pass
a set ofsimple "elimination-by-aspects"constraints
(Tversky, 1972) in figure la. They then must have
a need or motivation to use either or both kinds of
fertilizer (figure lb). They then pass to a set of
resource constraints specific to each kind of fer-
tilizer, and will use that kind if they satisfy or pass
each constraint (figures lc and ld). Farmers will
use both kinds of fertilizer if they think the crop
needs both, and they pass both sets of resource
constraints.
Figure la.The DecisionBetween Organic and Chemical Fertiliz-
ers on Local Maize: Stage 1 Constraints.
40 cases
$
{Apply organic; chemical; both}
,Does your soil need
or respond to either chemical
, Eliminate
or organic fertilizers?
no
Both
I
0 cases
yes
l
2Does your local maize
variety need either chemical or • Eliminate
organic fertilizers?
no
Both
I
0 cases
yes
1
~Do you let most of your land
lie fallow for two or more years ....
at
a time so that afterwards you--*~nnunate
don't need to apply chemical or yes Both
organic fertilizers?
0 cases
no, I need them
afterwards
]
to
apply either chemical or
no Both
• Eliminate
organic fertilizers this year?
8 cases
I
yes
Elimination
Criteria
Farmers must first pass a simple set of con-
straints in figure la which eliminate use of both
organic and chemical fertilizers if: their type of soil
doesn't need or respond to either kind of fertilizer
(criterion 1), or their type oflocal maize seed doesn't
need either kind of fertilizer (criterion 2), or they
let most of their land lie fallow for two or more
years so that after the fallow period it doesn't need
either chemical or organic fertilizers (criterion 3),
or they lack the cash or credit for either chemical
or organic this year (criterion 4). If a farmer is
eliminated at this first stage ofthe decisionprocess,
he or she doesn't have to decide between organic
and chemicalfertilizersbecause both are eliminated
and the decision is simple.
Go on to figure lb
32 cases
fertilizer (criterion 6) and it is more profitable than
organic, they are sent only to figure ld.
In figure lc, farmers will apply organic fertilizer
to local maize if: they have enough animals to make
enough manure or compost to use on their local
maize (or a crop (e.g., tobacco) rotated with local
maize)8 every two or three years (criterion 8), or
they can buy the manure/compostthey need (criter-
ion 9) and they have or can borrow/rent an oxcart
and oxen to carrythe manure/compostto their fields
(criterion 10), and they have the time or (fun- or
part-time) labor to carry it to their fields (criterion
12), which are not too far away to reach by oxcart
(criterion 11). If all these constraints are passed,
the model predicts that the farmer applies manure
and/or compost to localize maize (or a crop rotated
with local maize). If a farmer fails one constraint,
the model predicts no manure/compost is applied.
In figure ld, farmers will apply chemical fer-
tilizer to local maize if: there was chemicalfertilizer
available at the time needed, either to buy or get
on credit (criterion 13), and the farmer had either
Stage
Two-Criteria
Farmers who pass "stage 1" criteria do have a
complicated decision, however, and continue on to
stage-two criteria in figure lb. If they think that
their local maize variety needs both kinds of fer-
tftizer to produce good yields (criterion 5) they are
sent on to both sets ofresource constraints in figures
ic and ld. If they think
local
maize needs only or-
ganic fertilizer (criterion 6) and it is more profitable
than chemical, they are sent only to figure lc. Simi-
larly, if they think
local
maize needs only chemical
36
'Have the [cash or credit
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