# Modelling savings behavior of agents in the kinetic exchange models of market

**ABSTRACT** Kinetic exchange models have been successful in explaining the shape of the income/wealth distribution in the economies. However, such models usually make some ad-hoc assumptions when it comes to determining the savings factor. Here, we examine a few models in and out of the domain of standard neo-classical economics to explain the savings behavior of the agents. A number of new results are derived and the rest conform with those obtained earlier. Connections are established between the reinforcement choice and strategic choice models with the usual kinetic exchange models.

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**ABSTRACT:**The distributions of income and wealth in countries across the world are found to possess some robust and stable features independent of the specific economic, social and political conditions of the countries. We discuss a few physics-inspired multi-agent dynamic models along with their microeconomic counterparts, that can produce the statistical features of the distributions observed in reality. A number of exact analytical methods and solutions are also provided. --SSRN Electronic Journal 01/2009; - SourceAvailable from: Arnab Chatterjee[Show abstract] [Hide abstract]

**ABSTRACT:**Increasingly, a huge amount of statistics have been gathered which clearly indicates that income and wealth distributions in various countries or societies follow a robust pattern, close to the Gibbs distribution of energy in an ideal gas in equilibrium. However, it also deviates in the low income and more significantly for the high income ranges. Application of physics models provides illuminating ideas and understanding, complementing the observations. Comment: 15 pages, 20 eps figures, EPJ class; To be published as "Colloquium" in Eur Phys J BPhysics of Condensed Matter 09/2007; · 1.46 Impact Factor - SourceAvailable from: Barkley Rosser[Show abstract] [Hide abstract]

**ABSTRACT:**This Colloquium reviews statistical models for money, wealth, and income distributions developed in the econophysics literature since the late 1990s. By analogy with the Boltzmann-Gibbs distribution of energy in physics, it is shown that the probability distribution of money is exponential for certain classes of models with interacting economic agents. Alternative scenarios are also reviewed. Data analysis of the empirical distributions of wealth and income reveals a two-class distribution. The majority of the population belongs to the lower class, characterized by the exponential ("thermal") distribution, whereas a small fraction of the population in the upper class is characterized by the power-law ("superthermal") distribution. The lower part is very stable, stationary in time, whereas the upper part is highly dynamical and out of equilibrium. Comment: 24 pages, 13 figures; v.2 - minor stylistic changes and updates of references corresponding to the published versionReview of Modern Physics 05/2009; · 42.86 Impact Factor

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arXiv:1006.5044v1 [q-fin.TR] 25 Jun 2010

Modelling savings behavior of agents in the kinetic exchange models of market

Anindya S. Chakrabarti

Indian Statistical Institute, 203 B. T. Road, Kolkata-700108

(Dated: June 28, 2010)

Kinetic exchange models have been successful in explaining the shape of the income/wealth dis-

tribution in the economies. However, such models usually make some ad-hoc assumptions when it

comes to determining the savings factor. Here, we examine a few models in and out of the domain

of standard neo-classical economics to explain the savings behavior of the agents. A number of

new results are derived and the rest conform with those obtained earlier. Connections are estab-

lished between the reinforcement choice and strategic choice models with the usual kinetic exchange

models.

PACS numbers: 89.65 Gh Economics, Econophysics- 05.20 Kinetic theory- 05.65.+b Self-organized systems

I.INTRODUCTION

The distributions of income and wealth have long been

found to possess some robust and stable features inde-

pendent of the specific economic, social and political con-

ditions of the economies. Traditionally, the economists

have preferred to model the left tail and the mode of the

distributions of the workers’ incomes with a log-normal

distribution and the heavier right tail with a Pareto dis-

tribution. For a detailed survey of the distributions used

to fit the income and wealth data see Ref. [1]. However,

there have been several studies recently that argue that

the left tail and the mode of the distribution fit well with

the gamma distribution and the right tail of the distribu-

tion follows a power law [2–4]. It has been argued that

this feature might be considered to be a natural law for

economics [2, 5].

The Chakraborti-Chakrabarti [6] model (CC model

henceforth) can explain the gamma-like distribution

very well whereas the Chatterjee-Chakrabarti-Manna [7]

model (CCM model henceforth) explains the origin of

the power law. Both models fall in the category of the ki-

netic exchange models. However, the first model assumes

a constant savings propensity and the second model as-

sumes an uniformly distributed savings propensity of the

agents. It may be noted that the distribution of the sav-

ings factor is exogenous in these models. It is not derived

from any optimization on the agent’s part or by some

other mechanism.

Ref. [8] considers an exchange economy populated

with agents having a particular type of utility function

and derives the CC model in the settings of a competitive

market. Here, we show that the same methodology could

be applied to derive the CCM model and we can explain

the savings factor accordingly. A further possibility is

also studied where the constancy of the savings propen-

sity over time is relaxed. More specifically, we examine

the cases where the savings propensity is dependent on

the current money holding of the corresponding agent.

[16] email-id : aschakrabarti@gmail.com

This model, as we shall see, shows self-organization and

in some cases, it gives rise to bimodality in the money

distribution.

It is well known the models of utility maximization has

been severely critisized on the grounds of limitations of

computational capability of human beings [9]. Hence,

to model the savings behavior of the agents, we study

some simple thumb-rules and derive the distributions of

savings therefrom and finally the income/wealth distri-

bution. In particular, we consider the savings propensity

as a strategy variable to the agents. Two cases are ex-

plored here. In the first case, the agents take their savings

decisions of their own by reinforcing their choices. In the

second case, the agents adopt the winning strategy. In

all cases, we study the final money distributions.

The plan of this paper is as follows. In section, II we

explain the savings behavior of the agents using argu-

ments from neoclassical economics. In the next section,

we study a self-organizing market where the savings is a

function of the current money holding of the correspond-

ing agent. In section V and VI, we study the savings

behavior of the agents where the savings decision follows

some very simple thumb-rules. Then follows a summary

and discussion.

II.KINETIC EXCHANGE MODELS IN A

COMPETITIVE MARKET

By competitive market we mean a market with atomistic

agents who trade with each other knowing that their in-

dividual actions can not possibly influence the market

outcome (that implies there is no strategic interaction be-

tween the agents). We assume that markets are always

cleared by equating supply and demand; that is the mar-

ket is completely free of any friction. Below, we elaborate

on the market structure and the behavior of the agents

more explicitly.

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2

A.The Chakraborti-Chakrabarti (CC) Model

This model considers homogenous agents characterized

by a single savings propensity.

derivation of the model following Ref. [8]. The structure

of the economy is the following. It is an exchange econ-

omy populated with N agents each producing a single

perishable commodity. There is complete specialization

in production which means none of the agents produce

the commodity produced by another. Money is not pro-

duced in the economy. All agents are endowed with a

certain amount of money at the very begining of all trad-

ings. Money can be treated as a non-perishable com-

modity which facilitates transactions. All commodities

along with money can enter the utility function of any

agent as arguments. These agents care for their future

consumptions and hence they care about their savings

in the current period as well. It is natural that with

successive tradings their money-holding will change with

time. At each time step, two agents are chosen at ran-

dom to carry out transactions among themselves compet-

itively. We also assume that the preference structure of

the agents are time-dependent that is the parameters of

the utility functions vary over time (Ref. [10, 11]). For

a detailed discussion on the derivation of the resulting

money-transfer equations, see Ref. [8]. Below, we pro-

vide the formal structure and the solution to the model

only.

Let us assume that at time t, agent i and j have been

chosen. Also, assume that agent i produces Qiamount

of commodity i only and agent j produces Qj amount

of commodity j only and the amounts of money in their

possession at time t are mi(t) and mj(t) respectively (for

simplicity, mk(0) = 1 for k=1,2). Notice that the notion

of complete specialization in production process provides

the agents with a reason for trading with each other.

Naturally, at each time step there would be a net transfer

of money from one agent to the other due to trade. Our

focus is on how the amounts money held by the agents

change over time due to the repetition of such a trading

process. For notational convenience, we denote mk(t+1)

as mkand mk(t) as Mk(for k = 1,2).

Utility functions are defined as follows.

i,Ui(xi,xj,mi)=xαi

Uj(yi,yj,mj) = yαi

jwhere the arguments in both

of the utility functions are consumption of the first (i.e.,

xiand yi) and second good (i.e., xjand yj) and amount

of money in their possession respectively. For simplic-

ity, we assume that the sum of the powers is normal-

ized to 1 i.e., α1+ α2+ λ = 1.

ity prices to be determined in the market be denoted

by pi and pj. Now, we can define the budget con-

straints as follows. For agent i the budget constraint

is pixi+ pjxj+ mi≤ Mi+ piQiand similarly, for agent

j the constraint is piyi+pjyj+mj≤ Mj+pjQj. In this

set-up, we get the market clearing price vector (ˆ pi, ˆ pj) as

ˆ pk= (αk/λ)(Mi+ Mj)/Qkfor k = 1, 2.

By substituting the demand functions of xk, ykand pkfor

We briefly review the

For agent

for agent j,

ixαj

jmλ

1

and

iyαj

jmλ

Let the commod-

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0 0.5 1 1.5

m

2 2.5 3

P(m)

FIG. 1:

The usual CC model: money distribution among the agents.

Four cases are shown above, viz., λ = 0 (+), λ = 0.2 (×),

λ = 0.5 ( ∗), λ = 0.8 (?). All simulations are done for

O(106) time steps with 100 agents and averaged over O(103)

time steps.

k = 1, 2 in the money demand functions, we get the most

important equation of money exchange in this model. To

get the final result, we substitute αi/(αi+αj) by ǫ to get

the money evolution equations as

mi(t + 1) = λmi(t) + ǫ(1 − λ)(mi(t) + mj(t))

mj(t + 1) = λmj(t) + (1 − ǫ)(1 − λ)(mi(t) + mj(t))

(1)

where mk(t) ≡ Mk and mk(t + 1) ≡ mk (for k= i, j).

Note that for a fixed value of λ, if αiis a random variable

with uniform distribution over the domain [0,1−λ], then

ǫ is also uniformly distributed over the domain [0,1]. For

the limiting value of λ in the utility function (i.e., λ →

0), we get the money transfer equation describing the

random sharing of money without savings.

Interpretation of λ: Here, it is clearly shown that λ

in the CC model is nothing but the power of money in the

utility function of the agents and finally this turns out to

be the fraction of money holding that remains unaffected

by the trading action. However, in this form it can not

be directly interpreted as the propensity to save. Below,

we try to derive λ from an utlity maximization problem

while retaining the kinetic exchange structure and we

show that in this slightly alternative formulation, λ is

indeed the savings propensity as has been postulated.

B. The Chatterjee-Chakrabarti-Manna (CCM)

Model

As is clear from above, in the CC model the savings deci-

sion, the market clearence, the prices are all determined

at the same instant. But the savings decision is usually

made in separation. More specifically, we can model the

savings decision and the market clearence distinctly. The

CCM model takes into account the heterogeneity of the

Page 3

3

agents. In particular, it assumes that it is the savings

propensity of the agents which differs from each other.

To derive the same, we assume that the agents take de-

cisions in two steps. First, they decide how much to save

and in the second step, they go to the market with the

rest of the money and take the trading decisions.

Formally, we can analyze a typical agent’s behavior at

any time step t in the following two steps.

(i) Each agent’s problem is to make the decision re-

garding how much to save. For simplicity, we as-

sume that the utility function is of Cobb-Douglas

type. Briefly, at time t the i−th agent’s prob-

lem is to maximize U(ft,ct) = fλi

to ft/(1 + r) + ct = m(t) where f is the amount

of money kept for future consumption, c is the

amount of money to be used for current consump-

tion, m(t) is the amount of money holding at time

t and r is the interest rate prevailing in the market

which can be assumed to be zero in a conservative

framework. This is a standard utility maximization

problem and solving it by Lagrange multiplier, we

get the optimal allocation for the i−th agent as

f∗

t= λim(t) and c∗

decision is independent of what other agents are do-

ing. So now the agents will go to the market with

(1 − λi)m(t) only.

tc(1−λi)

t

subject

t= (1 − λi)m(t). Clearly, this

(ii) Now that each agent has made the savings deci-

sion, they can engage in competitive trade with

each other in the fashion descibed in subsection

IIA with λ → 0 (but λ ?= 0; it is a mathemati-

cal requirement). Note that the amount of money

used by the i-th agent is c∗

The resultant asset exchange equations are those

given by the CCM model [7].

t= (1 − λi)m(t) only.

mi(t + 1) = λim1(t) + ǫ[(1 − λi)mi(t) +

(1 − λj)mj(t)]

mj(t + 1) = λjm2(t) + (1 − ǫ)[(1 − λi)mi(t) +

(1 − λj)mj(t)]

(2)

Interpretation of λi: First we recall the solution of

the savings decision which is f∗

implies

t= λim(t). Note that it

λi=

f∗

t

m(t)

that is λi is nothing but the proportion of money kept

for future usage to the current money holding and this is

by definition the savings propensity.

III.

λ AS A FUNCTION OF MONEY: A

SELF-ORGANIZING ECONOMY

A distinct possibility is that the savings propensity

is a function of money-holding itself. To examine

1e-06

1e-05

1e-04

0.001

0.01

0.1

1

10

100

1 10 100

P(m)

m

FIG. 2:

The usual CCM model: money distribution among the

agents. All simulations are done for O(106) time steps with

100 agents and averaged over O(103) time steps. The

straight line is a guide for a power law with slope -2.

that case, we need λ:[0,∞)→[0,1].

are two possibilities.

an increasing or decreasing function of money-holding.

The simplest forms that we may assume are the following.

However, there

The savings propensity can be

(i) λt = c1e−(c2m(t))with c1 <1: Savings propensity

is a decreasing function of money holding. The re-

striction on c1 does not allow any agent to have

savings propensity equals to 1. The system shows

self-organization and assumes a stable probability

density function in the steady state (See Fig. 3

where, for purpose of illustration, c1has been kept

constant at 0.95). It is seen numerically that as

c2increases the distribution converges to an expo-

nential density function. In the other extreme, it

tends to the CC model with λ = c1. For moder-

ate values of c2, the distribution resembles gamma

function. For other values of c1 also, the system

behaves similarly. Note that since c1 is the max-

imum possible savings propensity, for a very low

value of it, the system becomes indistinguishable

from an exponential distribution. While it seems

counter-intuitive that savings propensity falls with

the money holding, this might in fact be possible

since poorer people can not take any chance to gam-

ble whereas richer people can.

(ii) λt= c1(1−e−(c2m(t))) with c1< 1: Savings propen-

sity is an increasing function of money holding. The

economy again organizes itself and the distribution

of money becomes stable over time. However, there

is something more. It is seen numerically that bi-

modality may apper spontaneously in the density

function of money. See Fig. 4. Ref. [12] discusses

such bimodal distribution of wealth (or money).

There a mixture of the agents was used where two

classes of agents were characterized by two different

and widely separated (but fixed!) savings propen-

Page 4

4

0

0.5

1

1.5

2

2.5

0 0.5 1

m

1.5 2

P(m)

FIG. 3:

Savings propensity is negatively related to the level of

money. All simulations are done for O(105) time steps with

100 agents and averaged over O(103) time steps. Here, c1

has been kept constant at 0.95 and c2 has been changed.

The plots include c2= 0.1 (+), 0.5 (×), 1 ( ∗), 2 (?), 4 (?).

It is seen that as c2 increases, the distribution becomes more

and more skewed finally converging to an exponential

density function.

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

0 0.5 1 1.5 2

m

2.5 3 3.5 4

P(m)

FIG. 4:

Savings propensity is positively related to the level of

money. All simulations are done for O(106) time steps with

100 agents and averaged over O(103) time steps. Here,

c1=0.95 and the curves are plotted for c2= 1 (+), 2 (×), 3

( ∗) and 4 (?). Bimodality is clearly seen in the distribution

of money.

sities. Such a segregated population gave rise to

bimodal distributions. However, such segregations

are exogenous since the λs are given from outside

the system. Here, however, we have a new model

in which savings decision is completely endogenous

and the economy organizes itself in such a way that

it gives rise to a class of bimodal distributions. It

may be noted that bimodality in the income/wealth

distribution has actually been observed in many

cases (see e.g., [13]).

It is seen numerically that bimodality appears for

c1 ≥ 0.92 and c2 ≥ 1. Another interesting fea-

ture of this model is that keeping c1 constant as

c2 increases, the monomodal distribution breaks

into a bimodal distribution which again becomes

a monomodal distribution for even larger values of

c2.For example, consider Fig.

maximum value of c2 considered is 4. But as c2

increases further, the distribution again becomes

monomodal. However, it should also be mentioned

that if c1 is too large (for example, if c1 ≥ 0.97),

then the system produces some strange-looking bi-

modal distributions.

4 in which the

IV.‘IRRATIONAL’ DECISION MAKING

The standard economic paradigm of market clearence via

utility maximization has been criticised on the grounds of

limitations of computational capability of human beings

[9]. The main challenge is to derive the homogeneity in

savings behavior of the agents from a very simple thumb-

rule such that the final distribution of income/wealth

looks realistic. A few realistic components of decision-

making are noted in Ref. [14, 15]

(i) Players develop prefernces for choices associated

with better outcomes even though the association

may be coincident, causally spurious, or supersti-

tious.

(ii) Decisions are driven by the two simultaneous and

distinct mechanisms of reward and punishment,

which are known to operate ubiquitously in hu-

mans.

(iii) Satisficing or persisting in a strategy that yields a

positive but not optimal outcome, is common and

indicates a mechanism of reinforcement rather than

optimization.

Of particular interest is item (iii) which goes directly

against the derivations stated above (see Section II).

V.FROM ONE TO MANY ...

To model the savings behavior of the agents, we now

make the following assumptions.

(i) The agents do not perform static optimization.

(ii) There is reinforcement in their decision-making

process.

(iii) The agents look for better payoffs. But eventually

each of them converges to a single and simple strat-

egy or thumb rule.

To incorporate the three above-mentioned assumptions,

we model the agents’ saving behavior by Polya’s urn pro-

cess [14]. The model is as follows. Consider the i−th

Page 5

5

0

0.5

1

1.5

2

2.5

3

0 0.1 0.2 0.3 0.4

savings propensity

0.5 0.6 0.7 0.8 0.9 1

P(savings propensity)

B

C

A

D

FIG. 5:

The savings distribution by reinforcement. Four cases are

shown. Case A: a=1, b=1. Case B: a=2, b=2. Case C: a=4,

b=4. Case D: a=4, b=2. The solid and the dotted lines

show the theoretical results (the β(a,b) distributions).

1e-06

1e-05

1e-04

0.001

0.01

0.1

1

10

1 10

m

100

P(m)

A

B

FIG. 6:

The steady state money distributions for two cases:- (A)

a=1, b=1 and (B) a=4, b=2. Case (A) produces the same

distribution of money as the CCM model (slope -2 in log-log

plot) whereas in case (B) we get a distribution with slope -3

in the log-log plot. The lines drawn have slopes -2 and -3

respectively. All simulations are done for O(106) time steps

with 100 agents and averaged over O(103) time steps.

agent. The choice is binary, he can take any of the two

decisions, to consume (c) or to save (s). His strategies

(c and s) such that c,s = 0,1 and c + s = 1. At each

instant, he chooses the values for c and s. Define Ct(St)

as the number of times c (s) has been assigned a value of

unity in t time periods. The savings propensity at time t

(that is λt) is defined as the ratio of Stto (St+Ct). We

can assume that initially S0= a and C0= b. The rein-

forcement mechanism is incorporated by assuming that

the probability of choosing s = 1 at any time t + 1, is

simply λt. Basically, it is the Polya’s urn model and the

famous result that follows from it is the following (Ref.

[14]). The random variable λtconverge almost surely to

a limit λ. The distribution of λ is β(a,b). (See Fig. 5 ).

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.5 1 1.5 2

m

2.5 3 3.5 4

P(m)

A

B

FIG. 7:

The steady state money distributions for two cases:- (A)

a = b; a, b→ ∞ and (B) a=4, b=4. Note that case (A) is

identical to CC model with λ = 0.5. All simulations are

done for O(105) time steps with 100 agents and averaged

over O(102) time steps.

A.Two limits

(i) (a=b=1): From the above result, λtconverges to λ

where λ ∼ uni[0,1]. The resultant distribution of

money follows a power law. This is the basic CCM

model. See Fig. 6.

(ii) (a=b; a,b→ ∞): λt converges to λ where λ is a

delta function at 0.5. This corresponds to a special

case of the CC model where λ = 0.5. See Fig. 7.

B.For moderate values of a and b

Clearly, (1<a, b<∞): This model gives the gamma-

like part as well as the Pareto tail of the income/money

distribution for different values of a and b. For example,

we show the results of two cases.

(i) (a=4, b=2): The resulting distribution of savings

propensity is clearly β(4,2). The distribution of

money in the steady state follows a power law with

a slope -3 in the log-log plot. See Fig. 6.

(ii) (a=4, b=4): The resulting distribution of savings

propensity is β(4,4). The distribution of money in

the steady state is gamma function-like. See Fig.

7.

VI.FROM MANY TO ONE ...

In Section V, we have discussed how one can derive a

set of distributed savings propensities starting from a

unique value. Here, we discuss the reverse side of the

same coin. We shall show that the agents with different