beta¶

Specifies the degree to which previous experiences (v-values) are considered in the decision function, in other words, how much weight is placed on the v-values in the decision function. A low value for `beta` means high exploration.

The value of the parameter `beta` is used in the decision function. See also mu.

The parameter `beta` can be either a single value, in which case the same weight is placed on all v-values, or specified per element-behavior pair. Wildcards (*) may be used to set several values at once.

Syntax¶

```beta = e1->b1: v1, e1->b2: v2, ..., en->bn: vn, default: d
beta = v1
beta = *->*:v1  # Same as above
beta = e1->*:v1
beta = *->b1:v1
```

where `v1,v2,...,vn` and `d` are scalar expressions.

Description¶

`beta = e1->b1: v1, e1->b2: v2, ..., en->bn: vn, default: d` sets the individual weight on

• v(e1->b1) to v1,

• v(e1->b2) to v2, …,

• v(en->bn) to v2,

and the weight for all other v-values to d.

• The specification is independent of the list order:

`beta = e1->b1:v1, e1->b2:v2, default:d`

is the same as

`beta = e1->b2:v2, default:d, e1->b1:v1`.

• `default` need not be specified if all possible combinations `element->behavior` are present in the list. For example,

```stimulus_elements = e1, e2
behaviors = b1, b2
beta = e1->b1:v11, e1->b2:v12, e2->b1:v21, e2->b2:v22
```

`beta = v1` sets the weight for all v-values to v1.

• `beta = v1` is the same as `beta = default: v1`.

Dependencies¶

• The properties `stimulus_elements` and `behaviors` must be specified before `beta`.

• Each stimulus element used in the specification of `beta` must be present in the parameter `stimulus_elements`.

• Each behavior used in the specification of `beta` must be present in the parameter `behaviors`.

Examples¶

```@variables x = 1
beta = element1->behavior1: x, element1->behavior2: x+1, default:x+2
```

sets the weight for v(element1->behavior1) to 1 and for v(element1->behavior1) to 2, and for the remaining possible element-behavior pairs to 3.

```beta = element1->behavior1: 0.5, default:0
```

sets the weight for v(element1->behavior1) to 0.5, and for the remaining possible element-behavior pairs to 3.

```beta = 0.1
```

sets the weight for all v-values to 0.1.