How People React to a Deadline: The Distribution of Registrations of Statphys 23

In Fig. 1 we show the number of registrations to Statphys 23

ull dots).

ch point corresponds to one day and the deadline $T^*$=March 31 was

e one corresponding to the early registration and abstract submission.

also plot the data corresponding to the a different conference

P2DS 17)

r which we have rescaled the total number of registration at its

n $T^*$.

e data of the two conferences are remarkably similar

d are characterized by an initial linear behavior followed

a strong increase near $T^*$.

is strong similarity suggests for a simple mechanism to describe

e response of the people to a deadline and we propose have a

mple model.

e basic idea is that the pressure you have to register is proportional

the inverse of the remaining time to the deadline.

is gives a probability, $p(t)$, to register at time $t$ that is $p(t)

proptorac1(T^*-t)$.

om this the number of the registrations at time $t$ is:

t)=C0^T^*p(t)dt=A(N_tot)T^*T^*-t).

onumber

e logarithmic singularity at the end is regularized by discretizing the

tegral with an interval of one day and the constant $A(N_tot)$

fixed by the total number of final registration $N_tot$.

one can see in Fig. 1 this simple model fits the

served behavior extremely well.

is permits to predict the total number of registrations

ready from the initial slope.

result that could have some practical interest.

e model only assumes that the probability to register

the same for the whole interval of the the remaining time.

this respect there is no real tendency to shift the registration

wards the deadline.

e increase of pressure is just due to the approaching of the deadline.

is situation may appear curious because one could have expected

stronger pressure to postpone the payment towards the deadline.

this respect, however, one should notice that the data in Fig. 1

fer only to the registration and not to the payment of the fee which

uld have been done also at a late time.

ull dots).

ch point corresponds to one day and the deadline $T^*$=March 31 was

e one corresponding to the early registration and abstract submission.

also plot the data corresponding to the a different conference

P2DS 17)

r which we have rescaled the total number of registration at its

n $T^*$.

e data of the two conferences are remarkably similar

d are characterized by an initial linear behavior followed

a strong increase near $T^*$.

is strong similarity suggests for a simple mechanism to describe

e response of the people to a deadline and we propose have a

mple model.

e basic idea is that the pressure you have to register is proportional

the inverse of the remaining time to the deadline.

is gives a probability, $p(t)$, to register at time $t$ that is $p(t)

proptorac1(T^*-t)$.

om this the number of the registrations at time $t$ is:

t)=C0^T^*p(t)dt=A(N_tot)T^*T^*-t).

onumber

e logarithmic singularity at the end is regularized by discretizing the

tegral with an interval of one day and the constant $A(N_tot)$

fixed by the total number of final registration $N_tot$.

one can see in Fig. 1 this simple model fits the

served behavior extremely well.

is permits to predict the total number of registrations

ready from the initial slope.

result that could have some practical interest.

e model only assumes that the probability to register

the same for the whole interval of the the remaining time.

this respect there is no real tendency to shift the registration

wards the deadline.

e increase of pressure is just due to the approaching of the deadline.

is situation may appear curious because one could have expected

stronger pressure to postpone the payment towards the deadline.

this respect, however, one should notice that the data in Fig. 1

fer only to the registration and not to the payment of the fee which

uld have been done also at a late time.