Farmers, Bandits, and Soldiers:
A Model for Addressing Peace Agendas
Khalid Saeed, Oleg Pavlov, Jeanine Skorinko, Alexander Smith*
Abstract
This paper models an economy of farmers, bandits and soldiers. In addition to the economic
factors affecting the economy studied by Saeed and Pavlov (2008), we examine also the effects
of two psychological factors broadly categorized as exposure to violence and group identity.
Within this environment, we conduct simulation experiments (1) infusing the economy with
additional agents of each type under different assumptions about rational economic behavior,
psychological influences and the writ of government, (2) changing the parameters representing
the productivities and behavioral scaling factors in the economy, and (3) adding external flows to
the incomes of each type of agent. Our experiments indicate that the optimal policy for
promoting peace involves increasing farmer productivity, strengthening the writ of government,
downplaying the prestige of soldiers and reforming institutions so corruption and the extraction
of ransom from the public are reduced.
Key words: peace economics, political economy, conflict resolution, behavioral economics,
psychology, public policy, system dynamics, computer simulation.
ectively, Professor, Associate Profe ‘. tant Professor and Assistant Professor at Worcester Polytechnic
Institute, Worcester, MA. Contact author: Khalid Saeed, email saeed@wpi.edu
1
Introduction
Drawing from a historical metaphor referred to as the “dynastic cycle” (Usher, 1989; Feichtinger
and Novak, 1994; Feichtinger et al., 1996), Saeed and Pavlov (2008) modeled an economy as
interplay of farmers, bandits and soldiers. Farmers grow crops, bandits plunder farmers and
soldiers protect farmers. The allocation of resources among these three institutions determines
levels of freedoms available to the public. Respective incomes of farmers and bandits define
economic legitimacy. Figure | shows a state space characterization of various observed
conditions. High levels of both freedoms and economic legitimacy in a community imply
presence of a de facto people power.
raj Bandit People
= Rule Power
a
&
io}
mot
oO
vo
ira
= Failed Authoritarian
g State Rule
LOW HIGH
Economic Legitimacy
Figure 1: Manifestations of a generic political system
Societies can also be positioned in the remaining three quadrants of the state space. High levels
of appropriations by a relatively small population of bandits from many working farmers imply a
de facto bandit rule. Examples are states with pervasive corruption and rampant underground
economy (i.e. low economic legitimacy) with a weak and corrupt government. In such an
economy, the public is freely exploited by corrupt officials and the mafia for ransom. A non-
legitimate economy with a low level of freedoms is the feature of a failed state. Freedoms are
limited due to the impositions made by the warlords and mafia leaders rather than by the
government. An authoritarian rule is characterized by the presence of legitimate economy and a
low level of freedoms. In an authoritarian system, the government exercises coercive imposition
of authority on public. All three combinations represent a potential for conflict, which threatens
political stability and peace.
Building on the metaphorical system presented in Saeed and Pavlov (2008), this paper attempts
to understand why political systems might tend towards the observed variety of political and
economic scenarios and what paths to change might exist. In addition to the purely economic
forces considered in the original model of Saeed and Pavlov, here we also review behavioral
factors and their impact on the dynamics of a political economy. The modified model allows us
to differentiate between primary forces driving internal trends in the political economy and the
influences which are mere facilitators of change.
Theories of social conflicts
Conflicts in social groups have been extensively studied by many disciplines and from various
perspectives. The traditional approach to modeling social conflict — whether the conflict is
political, economic, individual or military one — is to assume rationality of agents (Becker 1968;
Dixit 2004; Cooter and Ulen 2000). A social conflict is then a Coasian bargaining by strategic
adversaries (Bowles and Gintis 1990). This view reduces any conflict to a private cost-benefit
3
analysis by rational individuals. Faced with a choice between two activities -- production or rent-
seeking (Bhagwati 1982; Baumol 1990) -- an agent chooses to engage in opportunistic behavior
if the expected benefit from a criminal act is greater than the expected cost. In that context, a
robbery is a private negotiation and exchange of life for some amount of money (Vahabi 2010).
Many formal models of political conflict have been proposed based on rational analysis. There
also exist behavioral theories explaining irrational actions by social groups and institutions. In
the following subsections, we review a subset of such models and theories that are relevant the
model we propose.
Formal Models
There are several methodological approaches to modeling political and economic development
and the associated conflicts. Cioffi-Revilla (1998) provides a formal theory of political
uncertainty using propositional logic, set and probability theory, stressing the probabilistic nature
of political events and their probabilistic causality. Political behavior is modeled in multiple
dimensions. A war, for example, is described by the number of participating sides, the size of the
armies, the duration of the conflict and the casualties. Individual micro-level events are
aggregated to determine macro-level political behavior.
Acemoglu and Robinson (2006), in contrast, follow a game-theoretic approach, using methods of
dynamic optimization, in their “economic incentives-based” study of political development. The
rich, middle class and poor respond to economic incentives during conflict. The rich prefer a
dictatorship that ensures their political and economic superiority, but may need to concede power
to avoid the costs of being overrun by a revolution. Acemoglu and Robinson (2006) builds on
previous research (Acemoglu and Robinson, 2000) about why Western societies extended voting
rights in the nineteenth century (one argument is that it was a strategic decision to avoid
widespread social unrest). Relatedly, Acemoglu and Robinson (2001) develop a theory of
political transitions, where the threat of revolution can force elites to democratize. Aghion,
Alesina and Trebbi (2004) focus on the design of political institutions, modeling how much
power societies delegate to their leaders. A key decision is choosing the size of a minority
required to block legislation.
Hirshleifer’s (1988) general equilibrium framework has become the pre-eminent approach in
peace economics (Isard 1994; Caruso 2010), which focuses in the analysis of social conflicts.
Agents with productive and combative technologies choose between productive activities that
increase economic output and unproductive activities such as war and banditry. Many papers
extend the Hirshleifer model of conflict (see Caruso 2010, note 2), showing that the allocation of
resources between productive and unproductive activities and the relative sizes of the associated
sectors have long term economic implications (Caruso 2010). For example, Hirshleifer (1995)
writes about the breakdown of anarchy in favor of hierarchy, which leads to a higher proportion
of resources being used productively. Skaperdas (1992) studies conflict in the absence of
property rights, arguing that cooperation can occur if it is in the interest of all agents.
The system dynamics approach has also been used by several authors to study conflict. Relying
on military documents and extensive studies on counterinsurgency, Anderson (2011) builds a
factual system dynamics model of an insurgency conflict. While the model is calibrated to the
data from the Anglo-Irish War of 1919-21, it allows to draw conclusions that are appropriate for
modern conflicts. Coyle (1985) offered a qualitative model of insurgency. Grynkewich and
Reifel (2006) also treat insurgency using system dynamics. More general system dynamics
models of conflict and political instability have been presented in Saeed (1986), Saeed (1990)
and Pavlov et al (2005) that explain conflict as a function of role play by public, government and
dissidents.
Behavioral Theories
Several different sociological theories explain the existence and purpose of conflict, criminal
behavior, and violence. Hirschi (1969) argued in his control theory that our social bonds can
prevent us from committing crimes. In particular, he argued that there are four types of bonds
that are important. First, the more attachment we have to people and institutions, the lower the
likelihood that we will engage in asocial activities. For instance, the more young adults identify
with their parents or college institutions, the less likely they are to engage in criminal behaviors.
Second, the more individuals are committed to conforming to societal norms (e.g., getting an
education, getting a job, getting married, etc.), the less likely they are to engage in criminal
activity. Third, the more involved in different activities individuals are, then the less likely they
are to engage in criminal activity. And, fourth, the more people believe in the moral validity of
social rules, then the less likely they are to engage in criminal behavior.
Some theorists argue that criminal behavior and social unrest are beneficial to society because it
encourages social order (Durkheim, 1915/1966). More specifically, it is argued that criminal
activities will strengthen the bonds and solidarity between the law-abiding citizens. In addition,
it is argued that social unrest encourages and enhances conformity to socially “appropriate” or
normative behavior because people do not want to be labeled as being deviant (Tannenbaum,
1938). Finally, social unrest can eventually induce social change. For instance, the negative and
discriminatory behavior focused towards Blacks began to be questioned by Martin Luther King,
Jr. and others. Initially, their attempts at civil rights were met with ridicule, violence, and even
arrests. However, over time, with the Civil Rights Movement, there was social change.
In trying to understand why individuals shared unauthorized music online, it was found that if
the societal norm was that sharing music was not unethical, than there was an increase in sharing
behavior (Shang, Chen, & Chen, 2008). Relatedly, in terms of violence, the more immersed an
individual is in violent and criminal acts, then the more “normal” those types of behaviors seen,
and this too increases the likelihood that these behaviors will continue (Lee, et al., 2007; Logan,
2009; Sutherland, 1939; Wolfgang, 1958).
There has been growing evidence that real agents often do not act rationally (Sunstein 2000;
Parisi and Smith 2005; DellaVigna 2009). Thus, while formal models typically do not subsume
beliefs of agents, inclusion of cognitive aspects in formal models is likely to improve our
understanding of conflict (Caruso 2010). Hence, our model extends the interaction between
metaphorical farmers, bandits and soldiers (Saeed and Pavlov 2008) by incorporating the
psychological influences. This modification allows us to extend our policy space to also include
behavioral interventions.
The modified farmers, bandits, soldiers model
An aggregate view of our modified model is shown in Figure 2. It includes five sectors: political
economy, economic influences, behavioral influences, control and performance. This structure
resulted from: 1) reorganization of the Saeed-Pavlov model into a sector hierarchy and 2)
7
constructing a new sector incorporating behavioral influences. Following the norm in the original
model, we have continued to use linear behavioral relationships instead of non-linear graphical
functions, since there was no basis to justify any postulated nonlinear shapes. Also, as this model
is rich in balancing loops, the range of variation around the normal operating points in the
behavioral relationships is quite small and choosing linear behavioral relationships has little
effects on the dynamics generated by the model. Model equations can be found in the Appendix.
Each sector shown in Figure 2 is described below:
control y
behavioral influences 7” Political economy = economic influences 7
—
performance = 7
Figure 2: An aggregate view of the modified farmers-bandits-soldiers model
Political Economy Sector
The core of the model is the Political Economy sector (Figure 3). The economy is populated by
three metaphorical cross-sections of individuals: farmers, bandits and soldiers. Farmers produce.
Bandits loot. And soldiers defend farmers against bandits. Farmers may choose to become
bandits and bandits may choose to return to farming based on the relative income opportunities.
Soldiers are recruited from the farmer pool. When soldiers retire, they return to farming.
Farmers grow produce. Their total productivity depends on the total farmer population and the
amount of cultivated land. Bandits loot part of the farmer produce, and the amount of looting
can fluctuate year to year. It is greater when peasants are doing well, and smaller when the
economic conditions are poor. Besides looting, bandits extract rent through bribes and levies and
engage in forbidden production and services, such as gambling, gun running, prostitution,
smuggling and narcotic drugs. These are labeled in the model as nonlegit produce by bandits.
Historic earnings create a perception of the income that a bandit can earn. We model the
perception formation as a simple averaging process.
Existence of bandits poses threat to society. Soldiers attempt to control and limit the number of
bandits. The threat to society posed by bandits dictates the desired size of the army. The desired
number of soldiers is, however, constrained by the financial resources available for their support.
By paying taxes, farmers support an army of soldiers. More taxes allow a larger army. The cost
of the army determines the needed amount of tax. Tax collection can, however, deviate from the
needed amount based on economic conditions.
Bandits come from the ranks of farmers. Some farmers are encouraged to turn to banditry when
they perceive that banditry may provide them with better income than farming. Farmer
disposable income is the produce grown by them, less taxes paid for maintaining the army and
less the appropriations extracted by the bandits. The economic attractiveness of banditry is
measured by the farmer perceived relative income, which is given by the ratio of the perceived
disposable income per farmer to the perceived disposable income per bandit. It is assumed that
there are some normal flows between the populations of farmers and bandits, that is, there are
always some farmers turning to banditry and some bandits who return to farming. In a steady
state the two flows are balanced.
10
oy political economy ase
disposable additions —
income ceived disposable
chng it
disposable
disposable extemal assistance
income to bandits
extemal
assistance to
soldiers collection
normal well being ‘SW for
farmer income ofa farmer limiting loot
Figure 3: Political Economy Sector
14.
Economic Influences Sector
The Economic Influences sector (Figure 4) defines rational economic factors that affect the flows
between the populations of farmers and bandits. The variable farmer relative income is the ratio
of the perceived disposable income per farmer to the perceived disposable income per bandit.
The ratio captures the population’s perception about the economic advantages of becoming and
remaining a bandit.
The danger posed by bandits dictates how much of societal resources should be allocated to
soldiers who maintain order. The need is expressed as the variable desired number of soldiers, as
specified in the Political Economy sector. For a variety of reasons the true size of the army might
be different from desired. The variable solder availability measures the discrepancy between the
desired and true sizes of the army. It is coded as ratio of soldiers to the desired number of
soldiers.
economic influences Aas
q soldiers
a
relative i +
income
soldier availabilty
i + desired
perceived disposable number
income per farmer of soldiers
Figure 4: Economic Influences Sector
12
Behavioral Influences Sector
The behavioral influences sector (Figure 5) captures the psychological effects of violence and
group identity. The behavioral sector feeds into the political economy sector by affecting agents’
desires to change their status (farmer to a bandit or farmer to soldier). As this sector incorporates
new structure added to the model, we attempt to explain its logic in detail.
The larger the offending group, the larger the likelihood that violence will ensue. Consequently,
the magnitude of the threat is determined by the size of the threatening group (i.e., bandits) in
relation to the other parties (i.e., farmers and soldiers). Bandits pose a threat to society because
they commit violent acts as they appropriate loot from farmers. The amount of actual violence in
the society plays a role in the perceptions of threat.
Regardless of the actual amount of violence that occurs, and irrespective of the size of the
different societal groups, another factor that can influence the perceptions of the level of threat is
how accessible information about violent and threatening acts is on one’s mind (Krueger, 2000;
Tversky & Kahneman, 1973; Watt & Larkin, 2010). If incidents of bandits getting caught more
readily come to mind, then the perception will be that banditry is more risky than it might be;
whereas, if incidents of bandits getting away with their acts come to mind, then the perception
will be that banditry is less risky than it might be in actuality (Riddle, 2010). Thus, in our model,
the information effect captures the amount of information people receive about the occurrence of
violence. The time it takes for people to form a perception of violence is inversely related to the
information effect. The change in perceived violence is inversely related to the time it takes to
13
form a perception of violence indicating that as the time it takes to perceive violence decreases,
the change in perceived violence increases and vice versa.
14
behav ioral influences As
unconcious te bichange
sensitivity any conscious sensitivity
to violence
to violence
tise in perceived
perceived violence _violence
@
actual violence,
€ time to form farmer urge
O
threat perception of violence Semi to change
to society
violence
pes taeet croup identity —
ofbandits / 9°ouP identity
information effect of soldiers
total workforce
Figure 5: Behavioral Influences Sector
15
Desensitization towards a stimulus or behavior occurs once the stimulus or behavior is no longer
novel. With increased exposure to a stimulus or behavior, the stimulus or behavior becomes less
and less novel, and subsequently individuals are more likely to become desensitized (or less
likely to experience a strong reaction to the stimulus or behavior). The more desensitized an
individual becomes towards a stimulus or behavior, then the chances of being more approving or
accepting of the once-arousing stimuli or behavior increase. For instance, in one study half the
participants played a violent video game (e.g., Mortal Kombat) or a non-violent video game
(e.g., Tetris; Carnagey, et al., 2007). After playing the video games, all participants viewed
videos of real violence (e.g., shootings, physical fights, etc.), and the experimenters measured
participant’s arousal as they watched these violence scenes using galvanic skin response (a
measure of emotional arousal) and heart rates. Those who had played the violent video games
showed much less arousal when watching the violent scenes than those who had played the non-
violent video games, suggesting that the previous exposure to violence via the violent video
games desensitized participants in this condition toward the violent images viewed later.
Increased desensitization also leads to increased acceptance of the once arousing stimulus or
behavior, and may even lead to an increased likelihood of behaving in a manner consistent with the
once-arousing stimulus. For example, Anderson and Murphy (2003) found that participants who
played violent video games were more likely to behave more aggressively to an opponentina
subsequent task (by delivering more loud blasts of noise towards the opponent) than participants
who played non-violent video games. The findings of this research suggest that once individuals
become desensitized, they may be more willing to act in manners that coincide with the once-
arousing stimulus.
16
To model desensitization, we start with unconscious normal violence. Unconscious normal
violence is a function of the perceived level of violence. Unconscious sensitivity to violence is
proportional to perceived violence and inversely proportional to unconscious normal violence.
This captures the idea that as perceived violence rises, so too does unconscious sensitivity to
violence, but as unconscious normal violence rises over time, people unconsciously become less
sensitive to violence.
Increased exposure to an arousing stimulus or behavior (e.g., violent attacks on farmers), will
lead to increased desensitization towards this once arousing behavior. This increased
desensitization will make this once arousing stimulus — the violence against farmers -- seem
more acceptable. Gradually, conscious sensitivity to violence in society will be adjusted to the
levels of unconscious sensitivity levels. Subsequently this acceptance of violence will result in
more acts of violence against the farmers.
Each type of agent (farmer, bandit or soldier) has a sense of group identity associated with being
of a particular type. The extent to which individuals identify with their group can vary (Hogg,
1996). According to Social Impact Theory (Latane, 1981), several key factors play a role in how
strongly one identifies with and will conform to their group. First, the size of the group matters;
identity and conformity increase along with group size (Bond, 2004; Gerard, Wilhelmy, &
Conolley, 1968; Rosenberg, 1961). Second, the immediacy of the group plays a key role in
identity and conformity. Physically close groups result in a higher sense of group identity and
conformity. For instance, research looking at gang behavior shows that gang members have a
17
heightened sense of identity and cause more havoc when they are close to one another.
However, when gang members start to distance themselves from one another (e.g., get married,
get a job, go to prison, etc.,), then the sense of identity and level of conformity drops (Goldstein,
1994). To measure group identity, the strength of that group identity is inversely related to the
share of agents in the economy that are of the relevant type. So, if the share of farmers is small,
their group identity is strong, and so forth.
Each agent’s urge to change is proportional to conscious sensitivity to violence and inversely
proportional to group identity, since stronger group identity makes people less willing to
changes. Urge to change feeds into the economic sector by affecting the flows between farmers
and bandits in a way that recognition of violence encourages both farmers and bandits to change
while it enhances prestige of soldiers motivating farmers to be recruited as soldiers and
encouraging soldiers to continue.
Control Sector
In the section on the Political Economy sector we discussed that the stock soldiers influences the
bi-flow between the stocks bandits and farmers. The control is included in the bi-flow rate
formulation as a ratio of the stock of soldiers to the sum of all non-soldier populations, i.e., the
sum of soldiers and bandits. We named the ratio state control (Figure 6) to signify the functional
role of soldiers in the model.
18
oD =) control As
state
control
Figure 6: Control Sector
Performance Sector
There are two performance measures, “freedoms” and “economic legitimacy” (Figure 7).
Freedoms is an indicator of political power that farmers have in the society. It is approximated as
a ratio of farmers to the sum of bandits and soldiers:
freedoms = a * farmers / (soldiers + bandits)
Here parameter @ = y ( farmers’ | (soldiers’ +bandits’)) is a normalization constant, which
ensures that the value of the index is one in the steady state. Starred variables denote steady state
values. The steady state value of freedoms measures the status quo distribution of political power
between the societal groups. As the distribution of power changes, the index can either rise or
decline.
The economic legitimacy index compares the volume of economic activity by farmers to the
scope of the economic activity of bandits:
19
economic legitimacy = B * produce of farmers / bandit disposable income
The normalization factor = bandit disposable income’ / produce of farmers ensures that the
index is equal to one in the steady state. Starred variables denote steady state values. The
economic legitimacy index captures the relative scope of productive and unproductive activities
within an economic system, which is an important measure. The relative size of productive and
rent-seeking sectors has strong long-term political and economic implications (Mehlum et al
2003; Caruso 2010).
A phase plot of these two indices (Figure 1) defines the economic and political health of a
society or an organization. The lower left corner of the phase plot represents low economic
welfare and low level of freedoms as in a failed state; the upper right corner of the plot represents
higher economic welfare and higher level of freedoms as in a state enjoying people power. The
transition from one homeostasis to another may exhibit better-before-worse or worse-before-
better behavior when we simulate this model.
20
Eas) performance 46
economic
legitimacy
produce
of farmers
bandits
+
Figure 7: Performance Sector
Experiments with the model
The model is initialized in equilibrium which is disturbed in three ways for simulation
experiments: a) by infusing a fixed number of additional members into the various population
stocks and activating progressively the assumptions about rational economic behavior,
psychological influences and the writ of government; b) by changing the parameters representing
the various productivities and scaling factors; c) by adding external flows to the revenues of the
three institutions in the system; and d) collecting a set of interventions for best performance.
While the first set of experiments is primarily aimed at understanding the internal dynamics of
the resource allocation system, the later sets provide insights into the key entry points for change.
All sets can, however, be interpreted in terms of the related policy interventions.
21
A farmer infusion in the first set would correspond to population growth with fixed resources in
a political economy. Soldier infusion would imply expansion of government’s role in a political
economy. Bandit infusion would imply an externally supported growth in insurgent activity or a
growth of parasitic sub-economies subsuming corruption and illegal production of goods and
services. Similarly, changing productivities and scaling factors, both in the economic and
behavioral contexts, would translate into policy interventions that stimulate respective
technologies or influences role-playing.
External infusion of actors into the system
Figure 8 shows the phase plots resulting from infusing new members into the respective
population stocks with the flows between the stocks being governed only by gravity, i.e., each
flow is proportional to the stock it emerges from and no other influences exit in the system. This
system attempts to re-level itself as connected water troughs would so the population proportions
in each stock return to the same level — resulting in a homeostasis that delivers more or less the
original levels of economic legitimacy and freedoms. Adding soldiers, however, slightly
improves welfare in the short run before reverting towards original equilibrium.
22
Ss freedoms ¥. econo...itimacy: 1 - 2 - 3 -
1.49 !
w i
g |
5 i
o 1.04 H
o H
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Add soldiers
06 T T 7 1
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Figure 8: Restoration of initial equilibrium when flows between populations are driven by
gravity
Figure 9 repeats the first simulation experiment after activating the economic influences. Now,
actors decide whether or not to choose banditry based on the relative income. The soldier pool is
adjusted in response to soldier availability for containing the threat to society posed by the
bandits. In this simulation, not only the new homeostasis occurs at a lower level of legitimacy
and freedoms, the paths to it also become more complex, longer and to some degree exhibiting
greater differences in short-term and long-term impacts. Thus adding soldiers improves our
performance indices in the short run, but worsens them in the long run. Adding bandits worsens
performance indicators in the short run, but improves them in the long run. In all instances, the
system ends up in an equilibrium that is of lower than the first equilibrium since the structure and
23
parameters have not been changed and a higher population has to be supported by the same
amount of resources.
s
freedoms
Page 1
Nae ?
freedoms v. econo...itimacy: 1 =
Add farnjers
104 i sponeceansanal
“| Add bandits
Add soldiers
T T T 1
o4 1.0 16
economic legitimacy 14:41 AM Thu, Mar 10,2011
Untitled
Figure 9: Paths towards new equilibrium when economic influences are added to the model
If we add the impact of the writ of the government that would force bandits to reform and check
farmers from becoming bandits, the end homeostasis is as at an even lower level of welfare and
the adjustment paths become even more complex exhibiting greater oscillatory behavior as
shown in Figure 10. This happens as the conflict between economic and control forces builds up
leading to an overshoot and eventual adjustment to a new equilibrium.
Addition of behavioral mechanisms due to group identity and sensitivity to violence speeds up
adjustment and reduces overshoot. As shown in Figure 11, behavioral influences also slightly
24
improve the quality of the new homeostasis in the process as they provide alternative paths of
adjustment to a less conflict-prone reality.
Figure 10: Adjustment to a new homeostasis with gravity, economic and government writ added
to the model
25
Ss freedoms v. econo...itimacy: 1 - 2-3 -
freedoms
Add bandit:
4 wh fh cvcree iene fA 8
06:
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Figure 11: Adjustment to a new homeostasis with gravity, economic, government writ and
behavior mechanisms added to the model
It should be noted however that, adding only behavioral influences to the gravity mechanisms as
shown in the simulation of Figure 12 has only minor influence as the paths of adjustment do not
change substantially from those with flows driven only by gravity. While the basic gravity model
may provide a natural adjustment path in a physical system, all three social factors pertaining to
economic motivators, governance, and psychological influence are important parts of reality and
although they moderate one another’s influence they offer potential intervention points for
change. The following experiments explore such entry points
26
Ss freedoms v. econo...itimacy: 1 - 2-3 -
4
=
3
4
¢
a6 t t r 1
o4 10 1.6
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Figure 12: Recovery to almost original equilibrium with behavioral influences added to the
gravity flows
Changing productivities and scaling factors
Saeed and Pavlov (2008) reported policy experiments involving changing productivities and
scaling factors with their original model that did not incorporate behavioral influences. They
reported that while increasing farmer resources and productivity moved homeostasis towards the
upper-right people-power quadrant, increasing soldier pay and facilitating bandit loot and
productivity moved the equilibrium towards anarchy. Our experiments with the model
subsuming also the behavioral influences did not change this inference as shown in the phase
plot of Figure 13. However, when farmer resources and productivity were improved
concomitantly with reducing prestige of becoming a soldier, the new homeostasis is pushed
27
further into the people-power quadrant since it did not unnecessarily engaged resources into
soldiers and thus both freed them up for farmers while also increasing their freedoms, which is
"
shown in Figure 14. The prestige of being a soldier is embodied in the "farmer urge to change’
that encourages soldier recruitment and "soldier urge to change", which discourages soldier
attrition,
Ss freedoms v. econo...itimacy: 1-2-3 -4-5~
Ldquecees we
”
E
a 1.04
rd
£
roductiv
ost aL
o4 10 16
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Figure 13: Paths of change and new equilibria in complete model from changing productivities
and scaling factors
28
Ss freedoms v. econo...itimacy: 1 - 2 -
1 :2apaninapennnnnennetpannnnnpe
With soldier
: prestige
3 ar eereterest erm’ : “....w/o soldier
£ prestige
eee eeeeten eoeemtes Leena |
0.8 r , 1
1.0 1.5 2.0
Page 1 economic legitimacy 3:44 PM Thu, Mar 10, 2011
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Figure 14: Increasing resources and productivity of the farmers with and without emphasis on
soldier prestige
External assistance to actors
The third set of experiments was aimed at determining how external assistance would affect
paths to peace. These experiments are particularly relevant to foreign relations and stimulation of
democratization process that civil society aspires to spearhead. Figure 15 shows the impact of
external assistance in the form of a revenue stream respectively to farmers, bandits and soldiers
in our default models, which attaches a degree of prestige to the work of a soldier, so farmers
have a preference for being recruited for the job and those in the profession are reluctant to leave.
Giving external assistance to Farmers translates into helping civilian projects, which slightly
improves welfare right away, although it falls back to the original conditions. Increased income
of the farmers creates some bandit conversions, while it also enhances the opportunities for the
bandits to loot and soldiers to tax. The subsequent crowding of the farmer sector together with
29
the increase in its taxation both by farmers and soldiers draws down any increase in its income
that is now siphoned away by bandits and soldiers.
Ss freedoms v. econo.
imacy: 1 - 2-3 -
1154 ee wees
Help farmers
freedoms
8
i
Help bandits
oss r r T 1
0.75 1.00 1.25
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NaeF ? Untitled
Figure 15: Effect of external assistance to actors
Giving external assistance to bandits amounts to helping insurgent groups to wage resistance
against the government. This also makes banditry attractive to farmers who defect towards it.
More bandits not only loot the populace they also create justification for increasing taxes to hire
more soldiers to contain banditry. These actions decrease both freedoms and economic
legitimacy, thus moving the political system considerably towards a homeostasis of anarchy.
Giving external assistance to soldiers has a surprisingly benign effect in the long run by moving
the economic system to greater legitimacy, although in the short run, it would usurp some of the
freedoms since soldiers impose more controls. This is shown in the simulation of Figure e8.
30
These controls force bandits to reform and check farmers from defecting into banditry. Thus,
military and law-and-order-related assistance to government in fact does seem to be more
effective than economic aid in terms of its impact on enhancing people power.
s freedoms ¥. econo...itimacy: 1 - 2-3 -
1.155
”
5
M4 1,005
$
&
0.85 r r 1
0.75 1.00 1.25
Page 1 economic legitimacy 4:05PM Thu, Mar 10, 2011
Naat ? Untitled
Figure 16: Effect of external assistance to actors with high soldier prestige
The impact of assisting with law and order is further enhanced when less prestige is attached to
being a soldier as shown in Figure 16, since this guards against maintenance of excessive soldier
force that would reduce farmer income through taxation while also attracting them to defect to
banditry. Assisting bandits along with attaching prestige to soldiering however slightly checks
the slide to anarchy, since the two policies implemented together counterbalance the defections
to banditry by creating higher levels of recruitment to and lower level of layoffs from the soldier
pool.
31
Best policy set for creating a path to peace
Above experiments point to the futility of a variety of common sense measures deemed useful
for dealing with conflict in an unstable political economy. A small policy set, namely
improvement in productivity, assistance to strengthen law and order institutions and
downplaying the psychological prestige often attached to the role of law and order institutions
appear to help the path to people power. These policy options are further experimented with in
Figure 17. Productivity can be raised by the transfer of new technologies and creating an
organization for promoting participation of large cross-sections of the public in the economy as
discussed in Saeed (1994). Raising productivity alone (1), that immediately increases farmer
revenue, might however only enhance the opportunities for appropriations by bandits in the short
run. In the long run, bandits defect from their ranks to become farmers for economic reasons and
this raises economic legitimacy. As the defection also reduces the burden on law and order
institutions, they can be scaled back, thus enhancing freedoms too.
In this situation, assistance to strengthen law and order institutions that discourage farmers from
defecting to banditry and encourage bandits to become farmers (2) will limit initial plundering
opportunities and further enhance freedoms in the long run. Downplaying the prestige often
associated with the law and order roles in the chaotic situations (3) will additionally limit
oversubscription to law and order ranks that enhances the role of farming thus further improving
people power. Last but not least, when law and order is also directed at reducing corruption and
ransom extraction from public through exemplary and vigilant role played by law and order
institutions (4), the path to peace is further facilitated.
32
Ss freedoms ¥. econo...itimacy: 1-2-5 -4-
1.154 aa 4
ngthen governance
8
€
5
= 1.004
E
&
0.85:
1.00 J
Page 1 economic legitimacy 9:06 AM Sat, Mar 12, 2011
NaeF ? Untitled
Figure 17: Effective policy sets for paths to peace
Conclusion
In this paper, we model an economy populated by farmers, bandits and soldiers. In addition to
the economic factors affecting flows between the three roles, as analyzed in Saeed and Pavlov
(2008), we consider psychological factors. Specifically, exposure to violence and group identity
affect people’s decisions regarding the role they select for themselves. The extent to which
violence is perceived depends on the amount of information people receive about the occurrence
of violent acts, and sensitivity to the violence is a function of the amount of violence that people
believe to be “normal.” Individuals with a high reference level of violence are less sensitive to
the violence they perceive. As far as group identity, people have a sense of belonging from being
members of a particular group. The strength of the effect is inversely related to the group’s share
of the total population, so group identity is decreasing in the group’s share, capturing the idea
33
that small minority groups often have a stronger sense of group identity than members of the
majority.
Within our environment, we conduct three kinds of simulation experiments. First, we infuse
additional agents of each type under different assumptions about rational economic behavior,
psychological influences and the writ of government. Second, we change the parameters
representing the productivities and scaling factors in the economy. Finally, we add external flows
to the incomes of each type of agent. All three kinds of experiments produce results useful for
creating policy aimed at promoting a desirable combination of freedoms and economic
legitimacy.
For instance, we find that when flows between stocks are governed only by gravity, in the short
term, adding farmers increases freedoms, adding soldiers decreases freedoms, and adding bandits
decreases freedoms and economic legitimacy. In the long term, however, the equilibrium is the
same. When economic influences are activated, long term economic legitimacy declines, but
freedoms are unchanged when agents of any type are added to the economy. Activating the writ
of government creates a more complex adjustment path to an equilibrium with somewhat lower
freedoms and economic legitimacy. Finally, activating the behavioral influences causes the
adjustment to the new equilibrium to occur more quickly by reducing the tendency for the
economy to overshoot the new equilibrium.
Regarding the productivities of the three agents in our economy, changing the cost per soldier
has minimal impacts on the long run equilibrium. Increasing bandit loot and productivity, on the
34
other hand, leads the economy to an equilibrium characterized by anarchy, as both freedoms and
economic legitimacy decline. Raising farmer resources and productivity, in contrast, pushes us
into the “people power” quadrant, where freedoms and economic legitimacy are higher.
As far as providing external assistance to each of the three types of agents, helping farmers
slightly improves welfare right away, but then it quickly falls back close to the initial
equilibrium. This is the case with both high and low soldier prestige. Providing help to soldiers
creates increased economic legitimacy in the long run at the expense of short run losses in
freedoms. Helping bandits pushes the economy toward anarchy, although the effect is somewhat
less pronounced when there is prestige attached to being a soldier than when there is not.
Thus, our experiments suggest the following approach to policy promoting peace. Initially, it is
optimal to raise the productivity of farmers, taking the economy to an equilibrium where
freedoms and economic legitimacy are higher. However, this is not enough. Increasing farmer
productivity must be accompanied by strengthening the writ of government and then
downplaying the importance of soldiers. Finally, institutions must aim to reduce corruption and
the extraction of ransom from the public.
While the results of our experiments provide numerous useful policy recommendations and a
literal “how-to” guide for achieving greater freedoms and economic legitimacy, we identify two
potential paths for further research. The first involves modeling the longer term psychological
impacts of exposure to violence. In our model, violence has important psychological effects, but
we do not explicitly capture the long-term resentment that can build up after sustained conflict
35
between groups. Such resentment has the potential to propagate violence, perhaps with
retaliatory acts, long after the effects of the initial perturbations from the equilibrium have run
their courses.
Another extension is endogenizing the adoption of new technologies by farmers. In our model,
initial productivities are taken as given and any technological change is exogenous. The
extension would involve allowing farmers to make optimal choices regarding the use of new
technologies according to individual cost / benefit analysis. In such a model, looting by bandits
would reduce the incentive for adopting new technologies, reducing economic legitimacy from
the first best solution where farmers have established and enforced property rights and maximize
the amounts they produce.
In sum, we build upon a past model (Saeed and Pavlov, 2008) in order to understand why
political systems might, at times, tend towards different scenarios. In addition, we extend the
past model by investigating the impact that psychological factors have on the behavior of the
political economy. The results show that while economic factors primarily determine the
distribution of soldiers, bandits, and farmers, psychological factors can affect the speed of
change thus providing another policy lever to influence the transition form problematic
distribution to a healthier one.
36
Appendix: Model Equations
behavioral influences
conscious_sensitivity_to_violence(t) = conscious_sensitivity_to_violence(t - dt) +
(change_in_sensitivity_to_violence) * dt
INIT conscious_sensitivity_to_violence = |
INFLOWS:
change_in_sensitivity_to_violence = (unconcious _ sensitivity_to_violence-
conscious_sensitivity_to_violence)/time_to_change _ sensitivity
perceived_violence(t) = perceived_violence(t - dt) + (rise_in__perceived_violence) * dt
INIT perceived_violence = 1
INFLOWS:
rise_in__perceived_violence = (actual_violence-
perceived_violence)/time_to_form_perception_of_violence
actual_violence = threat_to_society*violence_per_threat
Bandit_urge__to_change = conscious_sensitivity_to_violence/group_identity_of_bandits
external_refernce = |
external_ref_wt = 1
farmer_urge_to_change = conscious_sensitivity_to_violence/group_identity_of_farmers
group_identity_of_bandits =
1/((bandits/total_workforce)/(INIT(bandits)/INIT(total_workforce)))
group_identity_of_farmers =
1/((farmers/total_workforce)/(INIT(farmers)/INIT(total_workforce)))
group_identity_of_soldiers =
1/((soldiers/total_workforce)/(INIT(soldiers)/INIT(total_workforce)))
information effect = |
soldier_urge_to_change = conscious_sensitivity_to_violence/group_identity_of_soldiers
time_to_change__ sensitivity = 2
time_to_form_normal_perception = 5
time_to_form_perception_of_violence = 1/information__ effect
total_workforce = bandits+farmers+soldiers
unconcious_normal_violence = SMTH3((1-
external_ref_wt)*perceived_violencet+external_ref_wt*external_refernce,time_to_form_normal_
perception)
unconcious__ sensitivity_to_violence = perceived_violence/unconcious_normal_violence
violence_per_threat = |
control
state__control = (soldiers/(bandits+farmers))/(10/(110))
37
economic influences
farmer_relative__ income =
perceived_disposable__income_per_farmer/perceived_disposable__income_per_bandit
soldier_availability = soldiers/desired__number__of_ soldiers
performance
economic_legitimacy = (produce__of_ farmers/bandit_disposable__income)/(120/10)
freedoms = (farmers/(soldiers+bandits))/(100/20)
political economy
bandits(t) = bandits(t - dt) + (bandit__recruitment_and_attrition + bandit_infusion) * dt
INIT bandits = 10
INFLOWS:
bandit__recruitment_and_attrition =
((farmers*.01*farmer_urge_to_change)/(farmer_relative__income*state__control))-
(bandits*.1*(farmer_relative__income*state__control*Bandit_urge__to_change))
bandit_infusion = PULSE(bandit_additions,0, 10000)
farmers(t) = farmers(t - dt) + (farmer__infusion - soldier_recruitment__and_attrition -
bandit__recruitment_and_attrition) * dt
INIT farmers = 100
INFLOWS:
farmer__ infusion = PULSE(farmer_additions,0,10000)
OUTFLOWS:
soldier_recruitment__and_attrition = .01*farmers*(IF
sw_for_psych_influence_on_soldiers_rec=1 THEN farmer_urge_to_change ELSE
1)/soldier_availability-.1*soldiers*(IF sw_for_psych_influence_on_soldiers_rec=] THEN
soldier_urge_to_change ELSE 1)*soldier_availability
bandit__recruitment_and_attrition =
((farmers*.01*farmer_urge_to_change)/(farmer_relative__income*state__control))-
(bandits*.1*(farmer_relative__income*state__control*Bandit_urge__to_change))
perceived_disposable__income_per_bandit(t) = perceived_disposable__income_per_bandit(t -
dt) +(chng_in__disposable_per_bandit) * dt
INIT perceived_disposable__income_per_bandit = 1
INFLOWS:
chng in disposable_per_bandit = (disposable_income__per_bandit-
perceived _disposable__income_per_bandit)/2
perceived_disposable__income_per_farmer(t) = perceived_disposable__income_per_farmer‘(t -
dt) +(chng_in_disposable_income_per_bandit) * dt
INIT perceived_disposable__income_per_farmer = 1
INFLOWS:
38
chng_in_disposable_income_per_bandit = (disposable__income__per_farmer-
perceived_disposable__income_per_farmer)/2
soldiers(t) = soldiers(t - dt) + (soldier_recruitment__and_attrition + soldier_infusion) * dt
INIT soldiers = 10
INFLOWS:
soldier_recruitment__and_attrition = .01*farmers*(IF
sw_for_psych_influence_on_soldiers_rec=1 THEN farmer_urge_to_change ELSE
1)/soldier_availability-.1*soldiers*(IF sw_for_psych_influence_on_soldiers_rec=] THEN
soldier_urge_to_change ELSE 1)*soldier_availability
soldier_infusion = PULSE(soldier_additions,0,10000)
bandit_additions = 0
bandit_appropriations = bandits*loot_per_bandit
bandit_disposable__ income =
bandit_appropriations+nonlegit_produce__by_bandits+external_assistance_to_bandits
cost_per_ soldier = 1.5
desired__number_ of soldiers = (tax__collection/cost_per__soldier)*threat_to_society
disposable_income__per_bandit = bandit_disposable__income/bandits
disposable__income__per_farmer = farmer__disposable__income/farmers
economic_well_ being of_a_farmer =
perceived_disposable__income_per_farmer/normal__farmer_income
external_assistance_to_bandits = 0
external_assistance_to_farmers = 0
external_assistance_to_soldiers = 0
farmer_additions = 0
farmer__disposable__income = produce__of__ farmers-tax__collection-
bandit_appropriations+external_assistance_to_farmers
farmer__ productivity = 1.2
labor_elasticity = 1-land_elasticity
land = 100
land_elasticity = .7
loot_per_bandit = typical_loot__per_bandit*economic_well_being_of_a_farmer/(if
SW_for_limiting loot=1 then state__control else 1)
nonlegit_produce__by_bandits = bandits*productivity__of_bandits
normal__farmer_income = |
produce__of farmers =
farmer__productivity*((farmers)“labor_elasticity)*((land)“land_elasticity)
productivity __of_bandits = .5
soldier_additions = 0
SW_for_limiting loot = 0
sw_for_psych_influence_on_soldiers_rec = 0
39
tax_need = soldiers*cost_per__soldier
tax__collection = tax_need*economic_well_being_of_a_farmer+external_assistance_to_soldiers
threat_to_society = (bandits/(farmers+soldiers)/(10/110))
typical_loot__per_bandit = .5
40
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