MONTE CARLO SIMULATION OF LANGUAGE CHANGE 
IN TIKOPIA & MAORI* 
by 
Sheldon Klein, Michael A. Kuppin 
& Kirby A. Meives 
Computer Sciences Department 
1210 W. Dayton Street 
University of Wisconsin 
Madison, Wisconsin 53706 
i. Introduction 
The use of Monte Carlo Simulation with micro socio- 
linguistic models permits testing of many hypotheses un- 
verifiable by any other known method. The methodology un- 
derlying the research described in thispaper in outlined, 
and, to some extent, justified in \[20-22\]. Basically, the 
technique requires a simulation model with the following 
subcomponents: 
a) A stochastic socio-demographic model of a speech 
community for the starting date of the simulation. 
This model governs the conversational interaction 
patterns among members. 
b) A metamodel of significant historical events and 
changes during the simulated time period for use 
in generating periodic revisions in the basic model 
mentioned above. 
Sponsored in part by the National Science Foundation 
and the Wisconsin Alumni Research Foundation. 
c) Individual models of members of the society in the 
form of dynamically modifiable inputs to the para- 
meters that serve as inputs to the rules of the 
basic model. The model of each individual also 
includes one or more grammars that may be filled 
with generative rules for several languages. 
d) A language learning component, both for children 
and adults. This module permits the generation and 
parsing of sentences using rules from the grammars 
of specified members of the simulation. The learn- 
ing component makes it possible for a child born 
during the simulation to acquire the language or 
languages of his speech community through conver- 
sational interaction with other members of the so- 
ciety, and permits an adult either to modify one 
of his grammars in response to some contemporary 
linguistic innovation, or to acquire a new lan- 
guage with rules stored in a separate list. The 
learning component to be used in the system is a 
greatly improved version of the AUTOLING system 
\[22 ,23 \]- 
• .t A preliminary testing of the slmulatlon method was success- 
fully carried out using a hypothetical speech community con- 
taining 15 adults and 5 children. \[ 21 \] The behavioral 
model was extremely simple, as were the grammars (limited 
to a tiny subset of English). The learning model was also 
simplistic, involving the actual borrowing of full-fledged 
rules rather than their synthesis from fundamental analytic 
heuristics. The goal of this test, to attain linguistic 
and social stability through several generations, was at- 
tained. It was important because it demonstrated control 
of the model as a preliminary to innovations that might 
introduce linguistic or social change. (The particular 
simulation used a different kind of phrase structure rule 
notation than we currently use.) 
Now our research is directed toward the testing of the 
methodology through simulation of language change in a real 
speech community, in sufficient depth and detail that the 
predictions of the simulation will be subject to emprical 
verification. In our preliminary search for a suitable test 
/ 
case we first selected the speech community on the island 
of Tikopia in the South Pacific. This community seemed 
ideal because of the existence of excellent functional 
ethnological studies by Raymond Firth that took place in 
1928-29, 1951 and 1962 \[ 7-11 \], and because Tikopia was 
virtually untouched by World War II. Both the pertinent 
detail of Firth's studies and the relatively restricted and 
documented foreign contacts during this period seemed ideal 
for our work, and we put some effort into desig~ling a simu- 
lation system that could handle Tikopian Society and yet 
4 
have a basic generality. Unfortunately, Firth was unable 
to supply us with his linguistic field notes for Tikopia 
(little else of a suitable nature exists%. 
We then decided to switch to a simulation of language 
change among the Maori of New Zealand. The documentation 
for this group is voluminous and covers several centuries. 
Of particular value is the existence of census data on the 
Mario dating back to the nineteenth century. The time 
scale and detail level of the Maori model must 
be of a coarser sort than for Tikopia because of computer 
time and space demands, for it must account for a popula- 
tion 40 to 90 times greater than that of Tikopia over a 
time period of perhaps 150 years. However, we found that 
the design of our simulation system needed little or no 
modification for the Maori. 
We explicate the representation of both soci~linguistic 
situations in Section 3 to provide the reader with insight 
into the methodology. 
2. Language Learning Component 
The language learning logic of the AUTOLING System will 
furnish the basis for the learning component of the simula- 
tion system. AUTOLING is an automated linguistic field- 
worker capable of learning generative grammars through tele- 
type interaction with a live human informant. The program 
5 
is operational on the Burroughs 5500 computer*, and has 
been successfully tested on selected problems in English, 
Latin, Roglai, Indonesian, Thai and German. The discovery 
methods are heuristic rather than algorithmic, and the 
system is under continued modification. One subcomponent 
is capable of learning context free phrase structure rules 
in response to informant inputs consisting of sentences 
segmented into morphemes. An attempt is made to parse each 
informant input sentence on the basis of the current tenta- 
tive grammar. If the rules are adequate, the program prints 
the fact in a teletype message. If no~ it posits rules 
that might enable the parsing process to be completed. 
These rules, and their more general ramifications for the 
grammar as a whole, are tested via productions offered to 
the informant for acceptability verification. Rejected 
sentences cause the newly posited rules to be discarded. 
Acceptance of false rules through incomplete testing can 
occur. At the present time, the program tests for such a 
possibility by attempting to parse various known illegal 
sentences. The most recently recorded ones are tested every 
time a new rule is coined. All illegal sentences are tested at 
periodic intervals. If the bad rules were coined too far in the 
Preliminary programming is in ALGOL for the Burroughs 
5500 computer, eventually the program will be shifted to 
the compatible Burroughs 6500. 
6 
past for correction, the program throws out its entire gram- 
mar, and reanalyses the entire corpus, using the illegal 
sentence responsible for the situation as one of the key 
controls on the new grammar. A later version of AUTOLING 
will make a stronger attempt to determine the specific cul- 
prit rules, and take corrective action in form of trans- 
formations or simple context-sensitive phrase structure 
rules. In fact, eventually, the system will learn a trans- 
formational grammar consisting of unordered phrase structure 
rules plus obligatory transformations that operate whenever 
conditions permit during the generation process. Also, a 
morphology learning component will be integrated int O the 
system. 
For the simulation system, the human informant is re- 
placed by another grammar associated with another member 
o~ the community. While the system will contain only one 
learning program with its associated parsing and generation 
routines, each grammar associated with each member of the 
community might~on various occasions, serve as the grammar 
in which learning takes place, or as the grammar used to 
accept or reject the productions of an 'embryonic ' grammar. 
Learning feedback in an adult-adult conversation will not 
occur as often as in a child-adult context. The exact 
circumstances under which an individual's grammar learns 
or teaches are determined by the socio-demographic model. 
Special features that must be added t~ or modified in 
the AUTOLING system include the following: 
a. Multilin~ual Dictionary: For Tikopia, a list of 
Tikopian, English and Melanesian Pidgin morpheme equivalents. 
Any individual auditing new lexical items will add a list 
link in his grammar (which references terminal e~lements 
only indirectly) to the appropriate entries. Links to 
corresponding morphemes (if any exist) in other languages 
will be entered only if the person has actually been exposed 
to the form in conversation. 
For specialized vocabulary, the entries will also con- 
tain markers of the context in which the item is to be used. 
b. Sentence Generator: Both the Generator and the Parser 
use the same grammars. The generator selects non-terminal 
rewrite rules according to relative frequency parameters 
that are modified during the parsing process. Terminal 
elements are referred to by links to the dictionary. Some 
terminals are selected on the basis of the generation con- 
text, i.e., specialized vocabulary referring to items of 
material culture. Under some conditions, a terminal's 
translation equivalent in another language may be chosen. 
In the specialized case of normative learning, e.g., in 
a child-parent relationship, the generator will test newly 
formed rules by pertinent test productions offered to the 
normative teacher for acceptance or rejection. 
c. Parser: The parsing component may modify the fre- 
quency parameters pertinent to the generation process as a 
function of a particular rule's use in recent parsings. 
3. Modelling Tikopia and Maori 
Some generality in the system design would be necessary 
even if one intended to model only one society. In parti- 
cular, the rules governing the interaction of members of 
%he population would undoubtedly be subject to frequent 
revision during the course of research as it might become 
apparent that some variables modelled were not pertinent, 
ana that ommitted ones were significant. A fully general 
system, capable of modelling any society, must contain, 
implicitly, a universal theory of socio-linguistic behavior. 
A basic assumption of our system is that an individual's 
group memberships constitute the major determinants of his 
conversation behavior. Therefore it is essential that the 
system provide an efficient means of describing an individ- 
ual's age, sex, political, kin, work and social group member- 
ships as well as data of a purely geographic nature. 
Specifically, for Tikopia, it seemed that age, sex, 
village, clan, religion, household, marital state, work 
groups, and social status were the key variables governing 
conversational interaction . We planned to simulate 
a thirty or thirty-five year time span in a model contain- 
ing a population sample of about 120-165 people distributed 
9 
among three villages, representing about 1300 to 1800 people 
distributed among approximately 25 villages. 
The decision to construct the model with a few villages 
containing a large fraction of their real-world population 
(as opposed to more villages with fewer modeled people per 
village) was made on the basis of material contained in 
Firth \[ 7-11 \] indicating the village as the 
largest pertinent unit for our purposes. The decision to 
model three villages was based on the recognition of the 
subordinate, but real pertinence of inter-village relations. 
The problem of representing a complete multi-generation 
kinship structure for each individual also set a lower bound 
on the number of people per village. 
The actual method of crea}ing an initial population 
state is rather complex, and is described in Section 4. 
The researcher attempting to model Maori society is 
faced with the problem of finding pertinent data in a vast 
literature of essentially non-pertinent material. Fortu- 
nately, official government census information, dating back 
to the mid-ninteenth century, provide valuable demographic 
data. 
The population size demands a different kind of sampling 
than in the Tikopia model. The population ranged from 
56,000 in 1857-8 to 167,000 in 1961. A study of the litera- 
ture suggestS that the Maori-English linguistic acculturation 
i0 
phenomena might best be modelled in the following way: 
a. Population: Sample size ranging from i00 to 300 
Maori plus English speakers. 
b. Geographical Distribution: Two communities remote 
from white contact, plus the graduate creation of 
a city population group, and a group in an inter- 
mediate location. 
c. Key Social Variables: Tribe, hap~ or tribal sub- 
group, social class (aristocrat or commoner~ age group 
(child, young unmarried, young married to middle aged, 
elder) lineag~ work groups or occupatio~ and relig- 
ion. The hap~, rather than the immediate family, 
appears to be the minimal significant social unit 
of organization for the goals of our simulation. 
In the case of city dwelling Maori, residence in 
the same city constitutes another group membership. 
d. Meta-model of Historical Change: Gradually in- 
creasing contact with English speakers, wars, grad- 
ual migrations to urban areas. 
4. Systgm Organization and Construction of the Data Base 
The learning program, as it stands, demands an inter- 
action between a live informant and teletype outputted 
questions. It is necessary, for the purpose of reducing 
the enormous computer time required for the successful 
ii 
simulation of change in linguistic patterns, to be able to 
break the current program into two parts -- one part that 
can read sentences input to it without asking for immediate 
help, and another which will generate sentences randomly, 
based on the rules that were formulated during the input 
stage. 
The portion of the program that is responsible for the 
generation of random sentences will also determine the con- 
text in which the sentence was spoken. Context is deter- 
mined by defining the subclass of persons who would be 
listening to this sentence, and placing an indicator of this 
subclass in the file of sentences which are generated. The 
sentences will be placed in a file, that will later be 
passed against all individuals in the sample in order that 
particular aquaintances are able to "hear" what was said-- 
at the same time creating rules which shall be used in the 
next generation pass. 
At major points in the process, events take place that 
need not be thought of during the normal cyclic activity. 
These involve the life and death routines, marriage cere- 
monies, arrival-departures, and recreation of the aquain- 
tance lists that describe who is listened to. Because of 
the one-to-many character of speeches, it is possible to 
keep the aquaintance lists to a manageable size by listing 
only those persons whom one listens to, and not those who 
12 
are spoken to. 
Before we examine the conversation process further let 
us discuss the general problem of creating a sample for 
data that is available only in aggregate form. 
4.1 Sample Generation 
For many groups to be studied by the process described 
in this paper, samples do not exist. If any information 
exists at all about these groups it is often in the form 
of cross-tabulation tables published as an indication of 
census patterns, and is usually not given in its raw form. 
*The problem of creating a kinship structure is not of 
this uype. In the case of Tikopia it is essential to keep 
urack of kin relations with contemporaries that may owe 
unelr origin to links with common ancesters, perhaps 2 or 
3 qenerations removed, who may be deceased at the start of 
the slmulation. The best automated method we could devise 
involves running an accelerated, prefatory, partial simu- 
ia=Ion of the society beginning several generations before 
nhe official start date. The only aspects modelled would 
he those governing birth and death, residence change and 
marrlage rules. Initially, all individuals would be assumed 
zc De unrelated, and marriage would take place with rela- 
tlve freedom. As the prefatory simulation progreSSes through 
successive generations, kin ties are createdjand the free 
choice of spouses disappears. By the time the presimulation 
is completed, the original starting populatlon is aeao, and 
each member of the main simulation population has a complete 
and consistent set of kinship relations. The level of de- 
zail in the Maori situation does no~ demand this micro- 
computation of kinship (see Section 3). 
13 
To model groups of people where it is impossible to collect 
raw data because of expense, time, or other complications 
such as the passage of time rendering the sample change 
(historical groups), it is often necessary to create a 
sample of people artificially. Since any such attempt will 
result in an incorrect sample, it is important to realize 
this beforehand and be on guard when viewing the results 
of the study against arriving at conclusions which are in- 
valid. We can, however, obtain results that have some 
validity by restricting our discussion to those character- 
istics of the sample that we are able to insert into our 
sample creation process by the heuristic methods described 
below. We realize that heuristic processes are just that-- 
there is no real guarantee of success in creating a sample 
which is totally accurate. But by prefacing theresults 
of our study with this disclaimer, and restricting our 
stated conclusions to those population characteristics 
which we know to be true, useful research can be expected. 
We can illustrate the sample creation best by an immediate 
example. 
Suppose we are interested in the study of linguistic 
patterns as they are formed with respect to three variables-- 
age, sex, and marital status. It is necessary first to des- 
cribe the catagories that are important to us for 
each of the variables in the model. 
14 
If we posit that age does not influence linguistic patterns 
except in major catagories, we can break the ages into the 
three groups Young, Adult, and Elder. 
Since the other two variables Sex and Marital Status 
have well defined groupings (Male, Female; Married, Un- 
married), we can define our task with the following table: 
Marital Percent of 
Age Se___xx Status 
Young Male Married 
Adult Male Married 
Elder Male Married 
Young Female Married 
Adult Female Married 
Elder Female Married 
Young Male Unmarried 
Adult Male Unmarried 
Elder Male Unmarried 
Young Female Unmarried 
Adult Female Unmarried 
Elder Female Unmarried 
Population 
9 
9 
9 
9 
9 
9 
9 
9 
9 
9 
? 
Defining a population artificially for the requirements 
of the simulation process involves the accurate choice of 
percentages of the total population for each of the above 
permuted catagories of variables. This can be done in many 
ways. 
15 
i. By hand. The above percentages may be chosen by 
the researcher after careful reading of documents descri- 
bing population characteristics. 
2. By computer algorithm. There are often published 
statistics on populations that can be used to create appro- 
priate percentages. Cross-tabulation tables are the most 
fruitful in this attempt, as they often contain all of the 
necessary information within them. If they do not, other 
population statistics such as correlation matrices may be 
used ~.g., lacking a published table displaying the rela- 
tionship between Age and Income, a correlation coefficient 
of .46 is useful). Since some of the information may be 
either contradictory or of disproportionate value, it is 
necessary that a decision be made on the actual ~istribution 
characteristics. If tables are available showing the rela- 
tionships, they should be used. But if tables are not avail- 
able, or if the only available information about a particular 
relationship is in the form of another statistic, the prefer- 
able thing to do is to create the table by hand, based on 
research of the textaal material. 
For example, assume that we wish to build a file of per- 
sons as mentlon~d earlier. In reviewing the published tables, 
however, we cannot find a table relating Ag4~and Marital 
Status. We do find, on the other hand, that the correlation 
between Age and Marital Status is given as .43. Using this 
16 
information, together with research of the text, it may be 
possible to generate a table of the following form: 
Married Unmarried 
Young 2% 28% 
Adult 46% 16% 
Elder 3% 5% 
If we make no use of the knowledge of the correlation 
coefficient of .43 between Age and Marital Status, we may 
generate a sample that has serious faults. Not making 
use of it in this case would be similar to creating a table 
of the form: 
Married Unmarried 
Young 15% 15% 
Adult 32% 30% 
Elder 4% 4% 
(approximate correlation coefficient r = 0) 
This table is clearly incorrect, Marital Status should 
l 
not be distributed evenly with respect to age. 
If a process of random selection over the specified 
probability distributions (the relative frequency tables) 
is used to create the persons in our sample, it should be 
17 
possible to run a cross-tabulation on this data with the 
result being that we can reproduce the tables that we 
started with to create that data. 
After the process of sample creation is finished, we 
may produce a table of the form: 
Percent of 
Marital Population 
Age Se___xx Status (rounded) 
Young Male Married 0% 
Adult Male Married 22% 
Elder Male Married 2% 
Young Female Married 2% 
Adult Female Married 24% 
Elder Female Married 1% 
Young Male Unmarried 13% 
Adult Male Unmarried 9% 
Elder Male Unmarried 2% 
Young Female Unmarried 15% 
Adult Female Unmarried 7% 
Elder Female Unmarried 3% 
This table would imply the use, in part, of the marginal 
relative distributions: 
18 
~le 48% 
Female 52% 
Young 30% 
Adult 62% 
Elder 8% 
Married 51% 
Unmarried 59% 
It can be seen that since there are few (the number is 
rounded to 0%) young married males, more information was 
used to arrive at these values than merely the use of the 
marginal distributions. Their use alone would imply that 
1 there should be approximately 6~ % young married males. 
4.2 Aquaintance Lists. 
To model the linguistic patterns as they occur in the 
real world, it is necessary to account in some way for appro- 
priate dissemination of information by insisting that each 
person speak for the most part with the same persons he 
spoke to in the past. This is a tedious process if done 
dynamically at the time the conversations are to take place 
in the computer simulation, we can show that it is par- 
simonious to create an "acquaintance list" of those persons 
who are in frequent contact with each individual, and to change 
t this acquaintance list at more infrequent intervals. The 
acquaintance lists may be updated together with other major 
actions, such as the birth and death routines, arrivals and 
departures, and the occurance of natural phenomena such as 
seasonal change. 
19 
We may build the acquaintance list by a technique close- 
ly approximating that which occurs naturally by the "best 
fit" method in which two persons are said to be "acquain- 
tances" if they have various attributes in common -- they 
may live near each other, work together, or belong to the 
same social group. If many attributes are in common, then, 
these people will be very likely to be forced to speak to 
one another whether or not they might be classified correct- 
ly as "friends". 
More formally, we may define a person's attributes by 
his position in the sample space. For a sample of n 
variables, a person can be defined by t:he n-tuple (¥I,V2,..., 
Vn). By a simple calculus, we can map this point from the 
integer n-space into the boolean m-space, where m is 
greater than or equal to n , and each variable now has the 
value 1 if the persons can be characterized by the truth 
of this attribute, and 0 otherwise. For example, the 
variable Ag~ in our example abov~ would be changed from one 
variable with three values to three variables with two 
values each. From Age: l=Young, 2=Adult, 3=Elder, we 
would construct Young in Age: l=True, 0=False; Adult in 
Age: l=True, 0=False; Elder in Age: l=True, 0=False. 
A person in our sample can now be characterized by the 
b~olean m-tuple (BI,B2,...,Bm). In order to determine which 
attributes that two persons have in common, it is necessary 
20 
to ADD (multiply) these to boolean vectors together. The 
resultant vector has l's in the positions where the two 
persons origionally both had had l's, and no place else 
are there l's. 
To account for the disproportionate import of the fact 
that two attributes are in common, and in some instances 
to correct for the fact that persons may be more likely to 
be acquaintances if they do not have two particular attri- 
butes in common (e.g., Sex), the resultant vector is multi- 
plied by a third Weight vector W . 
The Resultant vector is summed to a scalar, and this 
number is compared to an externally specified "hit" value 
"H" to determine whether these two persons are said to be 
"acquaintances". Example: 
= (l,0,0,1,0,1,1,0,1,0,0,0,0,1,0,1,1,0,0,0,0,1,1) 
= (0,1,1,1,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,1) 
A--B = (0,0,0,i,0,0,0,0,0~0,0,0,0,0,0,i,0,0,0,0,0,0,i) 
= (4,4,5,4,5,4,4,4,-5,4,4,4,4,4,4,4,1,4,4,4,4,4,4) 
R=ABW = (0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,4) 
m 
X = ~ Ri=12 
i=l 
H = 35 
In this case we see that since the value of our calcu- 
lation X does not exceed or equal the hit value H , we 
reject these two persons as being acquaintances. This re- 
21 
jection can be easily changed into a more dynamic technique 
by the use of more sophisticated stochastic methods, such 
as the rejection being conditions on a random number ex- 
ceeding the difference between the numbers X and H . 
Further selection is necessary to determine one-sided re- 
lationships. It may be possible that A is an acquaintance 
of B (B listens to A) but B is not an acquaintance of 
A (A does not listen to B), for instance if A is a 
village chief, and B is a non-destinguished village mem- 
ber. 
4.3 Conversation Interaction 
The flow of the generation and parsing process is as 
follows (the only exceptions are in the case of normative 
learning were immediate auditor feedback is required): 
a. Conversation Creation: 
i. Generate all utterances from each grammar at 
one time, by passing the grammar file serially. 
A. The number of utterances for each pass is 
set as an external parameter °'S" . 
B. Generate "S" sentences in context C 1 . 
Generate "S" sentences in context C 2 . 
Generate "S" sentences in context C 3 . 
Generate "S" sentences in context C n 
2. Enter conversation creation routine. 
22 
The conversation creation routine will peruse 
the acquaintance lists of each persons to generate 
"listens" in the form of ordered triples (a,b,c), 
where 
a = ID of listener 
b = ID of Acquaintance 
c = Context of talk. 
This triple (a,b,c) will be placed in a file 
called the "listen" file. The "listen" file, when 
finished, will be a stack of entries in order by 
the first entry a i . 
a I b I c 1 
a 2 b 2 c 2 
a 3 b 3 c 3 
: : : 
a b c n n n 
3. Enter the routine which parses the sentence 
produced. 
b. The Parsing Process: 
For any two persons A and B, A Can listen to 
sentences produced by B in only one context. 
i. Bring in the grammar for person A from 
second-level memory 
2. Determine the address on second-level memory 
of the conversation specified by the triple (a,b,c) 
23 
and bring it into first-level memory (core). 
3. Parse, or "listen" to the sentence. 
4. Iterate on step 2 until all sentences are parsed. 
5. Put the new grammar for this person on second- 
level memory ..... 
6. Get the next grammar from second-level memory 
and go to step 2. 
7. If no next grammar, increment the time counter. 
8. If time to recreate the acquaintance lists or 
other major events such as birth/death routines and 
arrivals/departures do so. 
9. Iterate on step 1 until finished with entire 
simulation process. 
5. Interpretation of Results 
The key problem is determining the success or failure 
of a simulation. Assuming everything else has gone well, 
how does one compare the grammars of the population members 
to determine their mutual similarities and their relation 
to the language situation in contemporary, real world Maori 
Society? 
The design of the system offers a uniqu~ detailedsquan- 
titative method for determining the similarity of the com- 
petence of speakers. Every legal sentence ever generated 
in the course of the simulation is saved by the system. At 
24 
the end of the simulation (or some other time) each individ- 
ual must attempt to parse every legal sentence ever produced. 
Different individuals may expect to have varying degrees of 
success in their parsing attempts. Analysis of the results 
can offer a detaile~ objective picture of the dialect situ- 
ation on the basis of common success or failure in parsing 
particular sentences. These results may be correlated with 
any socio-demographic factors recorded in the data base of 
the model. 
Given these rssults, one may then send the same list of 
sentences to New Zealand, and have the analogous test per- 
formed on a sample of the Maori population, asking informants 
to indicate the legal and illegal sentences. 
The results of the live testing may then be compared 
with the simulation results. Thus, the Monte Carlo simu- 
lation approach appears to offer Linguistics a strong 
empirical methodology for testing otherwise unverifiable 
hypotheses. 
25 

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17. Holst, Halvor: 'The Maori Schools in Maori Education', 
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20. Klein, S.: Some Components of a Program for Dynamic 
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14 of Preprints of Invited Papers for 1965 International 
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21. Klein, S.: Historical Change in Language Using Monte 
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22. Klein, S.: Current Research in the Computer Simulation 
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23. Klein, S., Fabens, W., Herriot, R., Katke, W., & Towster, 
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27. Metge, Joan: A New Maori Migration: Rural and Urban 
Relations in Northern New Zealand, London School of 
Economics Monographs in Social Anthropology No. 27, 
Athlone Press and Melbourne University Press, 1964. 

Metge, Joan: The Maoris of New Zealand, Rutledge and 
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Miller, John: Egrl~ Victorian New Zealand, Oxford 
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Parr, C. J.: 'A Missionary Library, Printed Attempts 
to instruct the Maori', J.P.S., Vol. 70, No. 4, 1961, 
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No. 3, 1961, pp. 211-34. 

Sinclair, Keith: The Ori@in of the Maori Wars, New 
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Sorrenson, M. P. K.: 'Land Purchase Methods and their 
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1885', Studies of a Small Democracy (edited by Robert 
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Williams, H. W.: A Dictionary of the Maori Language, 
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Williams W. L. & Williams H. W., First Lessons in Maori, 
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Wright, Harrison M.: New Zealand 1769-1840: The Early 
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